Low Number Of Iterations Research Articles

Optimal operation of three-phase unbalanced active distribution system based on semidefinite relaxation and convex-concave procedure

The three-phase distribution optimal power flow (D-OPF) problem has attracted much attention in recent years due to practical requirements for managing distributed energy resources and multi-energy customers in unbalanced active distribution systems. To deal with the strong nonconvexity of the three-phase OPF problem, this paper develops the semidefinite programming (SDP) formulation of three-phase OPF problems considering the mutual coupling in multi-phase networks. Instead of directly dropping the rank-one constraints, a sequential convex optimization algorithm based on the convex-concave procedure (CCP) is proposed for recovering a feasible and optimal solution of three-phase power flow. The proposed iterative algorithm not only has high computational efficiency but also guarantees the optimality when the semidefinite relaxation or second-order cone relaxation method is inexact. Case studies on various IEEE testing distribution systems verify that the proposed algorithm recovers the feasible and optimal power flow solution. It also exhibits a relatively low number of iterations and short solving time in large-scale IEEE testing systems.

A comparative analysis of feature selection models for spatial analysis of floods using hybrid metaheuristic and machine learning models.

The research aims to propose a feature selection model for hydraulic analysis as such a model has not been proposed previously. For this purpose, hybrids of three metaheuristic algorithms, particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) with two machine learning models which are support vector machine (SVM) and K-nearest neighbor (KNN) are employed. The dataset considered was hydraulic having an association with flood and possessed topographic, geo-environmental, and human-induced variables. The dataset considered had multicollinearity heteroscedasticity and autocorrelation problems. The metaheuristic algorithms were evaluated by varying the number of population size. Among them, PSO performed better by providing an appropriate number of features with a lower number of iterations. We have analyzed the performance of SVM with different kernels; linear, radial basis function (RBF), sigmoid, and polynomial, as the original SVM is designed only for linear datasets but the hydraulic dataset possesses non-linear characteristics as well. The performance of different kernels in terms of their accuracies is evaluated and recorded. This study showed that RBF performed the best and sigmoid showed the least accuracy for GA, PSO, and ACO algorithms. The performance of KNN is evaluated in terms of accuracies by varying the K-values. It was found that KNN shows low accuracy with a small K-value which then attained a maximum level by increasing K-values, and it finally started decreasing, explicitly, by further enhancing K-values. While comparing the performance of hybrids of GA, PSO, and ACO with SVM and KNN, it was analyzed that KNN performed better with these meta-heuristics with PSO-KNN which performed the best among the baseline models. Thus, the study proposes that PSO-KNN can be utilized as a feature selection technique to obtain optimal data subsets for hydraulic modeling and analysis.

Elementary flow mapping across life cycle inventory data systems: A case study for data interoperability under the Global Life Cycle Assessment Data Access (GLAD) initiative

PurposeLimited availability of life cycle assessment (LCA) data poses a significant challenge to its mainstream adoption, rendering it a central issue within the LCA community. The Global LCA Data Access (GLAD) network aims to increase the accessibility and interoperability of LCA data and offers benefits for different use cases. GLAD is an intergovernmental collaboration involving different stakeholders organized into working groups. The GLAD Nomenclature Working Group (NWG) developed a procedure and a set of criteria to map elementary flows among major nomenclature systems and reviewed bidirectional mappings. This paper provides an overview of the methodological approach followed by the NWG to achieve the resulting mapping files.MethodsThe mapping procedure involves several steps of flow and compartment matches and bilateral review. The procedure is supported by an ad hoc software tool called the “GLAD Mapper Tool” developed with the NWG and which is made available for free by the European Commission. The input files for the procedure are the properly formatted source and target flow lists and a file containing the mapping criteria. The four nomenclature systems mapped are those used in ecoinvent, Environmental Footprint, IDEA, and the U.S. Federal LCA Commons. The procedure included representatives from each of these nomenclature systems to ensure a multilateral agreement on the approach to verifying and assessing the quality of the results. The iterative mapping process included different stages of bidirectional reviews to achieve a balance between mapping coverage (i.e., percentage of source flows covered by the target list) and accuracy.Results and discussionThe mapping procedure proved to be an efficient approach for LCA practitioners in mappings between different nomenclature systems. After a relatively low number of iterations, mapping coverages higher than 90% were achieved, which is driven by the availability of unique substances (flow names) and the granularity of environmental compartments. Overall, none of the four flow lists achieved full coverage and the use of approximated matches (proxy matches) for environmental compartments and/or substances was necessary when a perfect matches between flows were not possible.ConclusionsThe NWG’s mapping activities may serve as a starting point towards defining a central hub for mapping impact assessment methods and datasets, improving data accessibility and interoperability for the LCA community as a step towards defining a unified nomenclature system. The GLAD mapping approach is open and transparent. The approach fosters traceability in the mapping process and offers the potential for greater interoperability across the LCA community, underlining the commitment to openness and collaboration.

Nonlocal damage evaluation of a sigmoid-based damage model for fibrous biological soft tissues.

The comprehension and modeling of the mechanical behavior of soft biological tissues are essential due to their clinical applications. This knowledge is essential for predicting tissue responses accurately and enhancing our ability to compute the behavior of biological structures and bio-prosthetic devices under specific loading conditions. The current research is centered on modeling the initiation and progression of soft tissues damage, which typically exhibit intricate anisotropic and nonlinear elastic characteristics. For this purpose, the following study presents a comparative analysis of the computational performance of two distinct damage modeling techniques. The first technique employs a well-established damage model, based on a piece-wise exponential damage function as proposed by Calvo et al. (Int J Numer Methods Eng 69:2036-2057, 2007. https://doi.org/10.1002/nme.1825 ). The second approach adopts a sigmoid function, as proposed by López-Campos et al. (Comput Methods Biomech Biomed Eng 23(6):213-223. https://doi.org/10.1080/10255842.2019.1710742 ). The aim of this study is to verify the validity of the López-Campos sigmoid-based damage model to be used in finite element simulation, the implementation of which is unknown. For this proposal, both models were implemented within a commercial Finite Element software package, and their responses to local and non-local damage algorithms were assessed in depth through two standard benchmark tests: a plate with a hole and a ball burst. The results of this study indicate that, for a wide range of cases, such as in-plane stresses, out-plane stresses, stress concentration and contact, all over large displacement conditions, the López-Campos damage model shows a good response to non-local algorithms achieving mesh independence and convergence in all these cases. The results obtained are in line with those obtained for the Calvo's damage model, showing, in addition, larger deformations under in-plane stress and stress concentration conditions and a lower number of iterations under out-plane stress and contact conditions. Consequently, the López-Campos' damage model emerges as a valuable and useful tool in the field of mechanical damage research in biological systems.

Scaled ILU smoothers for Navier–Stokes pressure projection

AbstractIncomplete LU (ILU) smoothers are effective in the algebraic multigrid (AMG) ‐cycle for reducing high‐frequency components of the error. However, the requisite direct triangular solves are comparatively slow on GPUs. Previous work has demonstrated the advantages of Jacobi iteration as an alternative to direct solution of these systems. Depending on the threshold and fill‐level parameters chosen, the factors can be highly nonnormal and Jacobi is unlikely to converge in a low number of iterations. We demonstrate that row scaling can reduce the departure from normality, allowing us to replace the inherently sequential solve with a rapidly converging Richardson iteration. There are several advantages beyond the lower compute time. Scaling is performed locally for a diagonal block of the global matrix because it is applied directly to the factor. Further, an ILUT Schur complement smoother maintains a constant GMRES iteration count as the number of MPI ranks increases, and thus parallel strong‐scaling is improved. Our algorithms have been incorporated into hypre, and we demonstrate improved time to solution for linear systems arising in the Nalu‐Wind and PeleLM pressure solvers. For large problem sizes, GMRESAMG executes at least five times faster when using iterative triangular solves compared with direct solves on massively parallel GPUs.

Matching-Aided-Learning Resource Allocation for Dynamic Offloading in mmWave MEC System

With exploiting massive spectrum resources, millimeter wave (mmWave) communications significantly improve the offloading capability for future mobile edge computing (MEC) techniques, which however is constrained by blockage problem in dynamic environments. In this paper, we study the resource allocation problem for the conceived mmWave MEC system with dynamic offloading process, in which the UEs are characterized by being mobile and having the imperfect knowledge of the offloading tasks coming. By introducing the multi-objective Markov decision process (MOMDP), the resource allocation problem is modeled by simultaneously minimizing the delay and energy consumption, where jointly considering the multi-beam assignment (mBA) and beamwidth and power optimization (BPO). To tackle this problem, we innovatively propose a matching-aided-learning (MaL) resource allocation scheme, with the aid of a learnable weight based attention mechanism (LW-AM) for adapting the dynamic offloading process. In particular, our MaL scheme includes many-to-one matching (M2O-M) based mBA algorithm and deep deterministic policy gradient (DDPG) based BPO algorithm, which are executed iteratively and converge with relatively low number of iterations. The simulation results show the practical value of the proposed MaL, which can approach the performance of benchmark scheme with perfect knowledge of offloading tasks.

Hierarchical iterative identification of output nonlinear Box-Jenkins Wiener model with ARMA noise

This paper focuses on development of a three-stage iterative identification algorithm for parameter estimation of a Wiener model with autoregressive moving average (ARMA) noise entering between linear and nonlinear part. The proposed three-stage algorithm is developed based on generalized extended gradient iterative (GEGI) algorithm to increase the convergence rate at a low number of iterations. To increase the convergence rate, a second algorithm is derived based on generalized extended least squares algorithm (GELSI). The proposed 3-stage generalized extended least square iterative (3S-GELSI) algorithm will also decrease the computational burden compared to the single-stage algorithm. Finally, to show that the discussed methods can identify the considered system effectively, simulation results of three examples are provided. The results demonstrate that the 3-stage GEGI (3S-GEGI) algorithm outperforms its basic single-stage counterpart in terms of convergence rate. Moreover, the proposed 3S-GELSI algorithm exhibits even greater superiority in terms of convergence rate compared to other existing methods. Additionally, it will be demonstrated that the computational burden decreases in the 3S-GELSI algorithm, as indicated by the reduced number of total flops.

Intelligent Resource Allocation in Backscatter-NOMA Networks: A Soft Actor Critic Framework

With the use of power domain non-orthogonal multiple access (NOMA) and backscatter communication (BAC), future sixth-generation ultra massive machine-type communications networks are expected to connect large-scale internet of things (IoT) devices. However, due to NOMA co-channel interference, the power allocation to large-scale IoT devices becomes critical. The existing convex optimization-based solutions are highly complex, and therefore it is difficult to find the optimal solution to the resource allocation problem in a highly dynamic environment. To alleviate this problem, this work develops an efficient model-free BAC approach with a NOMA system to assist the base station with complex resource scheduling tasks in a dynamic BAC-NOMA IoT network. The objective is to increase the sum rate of uplink backscatter devices. More specifically, we jointly optimize the transmit power of downlink IoT users and the reflection coefficient of uplink backscatter devices using a reinforcement learning algorithm, namely, the soft-actor critic (SAC) algorithm. With the advantage of entropy regularization, the SAC agent learns to explore and exploit the dynamic BAC-NOMA network efficiently. The proposed algorithm ensures the quality of service (QoS) requirements of downlink users while enhancing the sum rate of uplink backscatter devices. Numerical results reveal the superiority of the proposed algorithm over the conventional optimization (benchmark) approach in terms of the average sum rate of uplink backscatter devices. We show that the network with multiple downlink users obtained a higher reward with respect to a large number of iterations compared to episodes with a lower number of iterations. Moreover, the proposed algorithm outperforms the benchmark scheme and BAC with orthogonal multiple access in terms of the average sum rate with the different number of backscatter devices. Additionally, we show that our proposed algorithm enhances sum rate efficiency with respect to different self-interference coefficients and different noise levels. Finally, we evaluate and show the sum rate efficiency of the proposed algorithm with different QoS requirements and cell radii.

Thermal conductivity reconstruction method with application in a face milling operation

PurposeThis paper aims to reconstruct the spatially varying orthotropic conductivity based on a two-dimensional inverse heat conduction problem described by a partial differential equation (PDE) model with mixed boundary conditions. The proposed discretization uses a highly accurate technique and allows simple implementations. Also, the authors solve the related inverse problem in such a way that smoothness is enforced on the iterations, showing promising results in synthetic examples and real problems with moving heat source.Design/methodology/approachThe discretization procedure applied to the model for the direct problem uses a pseudospectral collocation strategy in the spatial variables and Crank–Nicolson method for the time-dependent variable. Then, the related inverse problem of recovering the conductivity from temperature measurements is solved by a modified version of Levenberg–Marquardt method (LMM) which uses singular scaling matrices. Problems where data availability is limited are also considered, motivated by a face milling operation problem. Numerical examples are presented to indicate the accuracy and efficiency of the proposed method.FindingsThe paper presents a discretization for the PDEs model aiming on simple implementations and numerical performance. The modified version of LMM introduced using singular scaling matrices shows the capabilities on recovering quantities with precision at a low number of iterations. Numerical results showed good fit between exact and approximate solutions for synthetic noisy data and quite acceptable inverse solutions when experimental data are inverted.Originality/valueThe paper is significant because of the pseudospectral approach, known for its high precision and easy implementation, and usage of singular regularization matrices on LMM iterations, unlike classic implementations of the method, impacting positively on the reconstruction process.

An Interior Point–Inspired Algorithm for Linear Programs Arising in Discrete Optimal Transport

Discrete optimal transport problems give rise to very large linear programs (LPs) with a particular structure of the constraint matrix. In this paper, we present a hybrid algorithm that mixes an interior point method (IPM) and column generation, specialized for the LP originating from the Kantorovich optimal transport problem. Knowing that optimal solutions of such problems display a high degree of sparsity, we propose a column generation–like technique to force all intermediate iterates to be as sparse as possible. The algorithm is implemented nearly matrix-free. Indeed, most of the computations avoid forming the huge matrices involved and solve the Newton system using only a much smaller Schur complement of the normal equations. We prove theoretical results about the sparsity pattern of the optimal solution, exploiting the graph structure of the underlying problem. We use these results to mix iterative and direct linear solvers efficiently in a way that avoids producing preconditioners or factorizations with excessive fill-in and at the same time guaranteeing a low number of conjugate gradient iterations. We compare the proposed method with two state-of-the-art solvers and show that it can compete with the best network optimization tools in terms of computational time and memory use. We perform experiments with problems reaching more than four billion variables and demonstrate the robustness of the proposed method. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. Funding: F. Zanetti received funding from the University of Edinburgh, in the form of a PhD scholarship. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0184 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0184 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Preconditioned successive over relaxation iterative method via semi-approximate approach for Burgers’ equation

<span lang="EN-US">This paper proposes the combination of a preconditioner applied with successive over relaxation (SOR) iterative method for solving a sparse and huge scale linear system (LS) in which its coefficient matrix is a tridiagonal matrix. The purpose for applying the preconditioner is to enhance the convergence rate of SOR iterative method. Hence, in order to examine the feasibility of the proposed iterative method which is preconditioner SOR (PSOR) iterative method, first we need to derive the approximation equation of one-dimensional (1D) Burgers’ equation through the discretization process in which the second-order implicit finite difference (SIFD) scheme together with semi-approximate (SA) approach have been applied to the proposed problem. Then, the generated LS is modified into preconditioned linear system (PLS) to construct the formulation of PSOR iterative method. Furthemore, to analyze the feasibility of PSOR iterative method compared with other point iterative methods, three examples of 1D Burgers’ equation are considered. In conclusion, the PSOR iterative method is superior than PGS iterative method. The simulation results showed that our proposed iterative method has low iteration numbers and execution time.</span>

Instance Segmentation for Feature Recognition on Noncooperative Resident Space Objects

Active debris removal and unmanned on-orbit servicing missions have gained interest in the last few years, along with the possibility to perform them through the use of an autonomous chasing spacecraft. In this work, new resources are proposed to aid the implementation of guidance, navigation, and control algorithms for satellites devoted to the inspection of noncooperative targets before any proximity operation is initiated. In particular, the use of convolutional neural networks (CNN) performing object detection and instance segmentation is proposed, and its effectiveness in recognizing the components and parts of the target satellite is evaluated. Yet, no reliable training images dataset of this kind exists to date. A tailored and publicly available software has been developed to overcome this limitation by generating synthetic images. Computer-aided design models of existing satellites are loaded on a three-dimensional animation software and used to programmatically render images of the objects from different points of view and in different lighting conditions, together with the necessary ground truth labels and masks for each image. The results show how a relatively low number of iterations is sufficient for a CNN trained on such datasets to reach a mean average precision value in line with state-of-the-art performances achieved by CNN in common datasets. An assessment of the performance of the neural network when trained on different conditions is provided. To conclude, the method is tested on real images from the Mission Extension Vehicle-1 on-orbit servicing mission, showing that using only artificially generated images to train the model does not compromise the learning process.

An Efficient Point-Matching Method-of-Moments for 2D and 3D Electrical Impedance Tomography Using Radial Basis Functions.

The inverse problem of computing conductivity distributions in 2D and 3D objects interrogated by low-frequency electrical signals, which is called Electrical Impedance Tomography (EIT), is treated using a Method-of-Moment technique. A Point-Matching-Method-of-Moment technique is used to formulate a global integral equation solver. Radial Basis Functions are adopted to express the conductivity distribution. Single-step quadratic-norm ( L2) and iterative total variation ( L1) regularization techniques are exploited to solve the inverse problem. Simulation and experimental tests on a circular reconstruction domain show satisfactory performance in deriving conductivity distribution, achieving a Correlation Coefficient ( CC) up to 0.863 for 70 dB voltage SNR and 0.842 for 40 dB voltage SNR. The proposed methodology with L2-norm regularization provided better results than traditional iterative Gauss-Newton's approach, whereas with L1-norm regularization it showed promising performance. Moreover, 3D reconstructions on a cylindrical cavity demonstrated superior results near the electrodes' planes compared to those of the conventional linearized approach. Finally, application to EIT medical data for dynamic lung imaging successfully revealed the breath-cycle conductivity changes. The results show that the proposed method can be effective for both 2D and 3D EIT and applicable to many applications. Strong conductivity variations are successfully tackled with a very good Correlation Coefficient. In contrast to conventional EIT solutions based on weak-form and linearization on small conductivity changes, the proposed method requires only one step to converge with L2-norm regularization. The proposed method with L1-norm regularization also achieves good reconstruction quality with a low number of iterations.

A Hierarchical Preconditioner for Wave Problems in Quasilinear Complexity

The paper introduces a novel, hierarchical preconditioner based on nested dissection and hierarchical matrix compression. The preconditioner is intended for continuous and discontinuous Galerkin formulations of elliptic problems. We exploit the property that Schur complements arising in such problems can be well approximated by hierarchical matrices. An approximate factorization can be computed matrix-free and in a (quasi-)linear number of operations. The nested dissection is specifically designed to aid the factorization process using hierarchical matrices. We demonstrate the viability of the preconditioner on a range of 2D problems, including the Helmholtz equation and the elastic wave equation. Throughout all tests, including wave phenomena with high wavenumbers, the generalized minimal residual method (GMRES) with the proposed preconditioner converges in a very low number of iterations. We demonstrate that this is due to the hierarchical nature of our approach which makes the high wavenumber limit manageable.

A perturbation-based acceleration for Monte Carlo – Thermal Hydraulics Picard iterations. Part II: Application to 3D PWR-based problems

This paper focuses on the application and demonstration of the GTF-based acceleration introduced in the first part of the article to three-dimensional problems. Two novelties are here introduced over the state-of-the-art. First, an operative definition of generalized transfer functions for three-dimensional distributions is introduced. Second, the GTF-based acceleration is used to speed up the Picard iteration scheme applied to 3D problems based on Pressurized Water Reactor (PWR) designs. Consistently with the mono-dimensional results presented in Part I of this article, the GTF-based acceleration allows to achieve convergence within a lower number of iterations, with a speed-up factor up to 5.5, compared to the original Picard iteration.

Optimal Power Flow through Artificial Intelligence Techniques

Context: The integration of optimization methods into the various processes carried out by an electric power system seeking energy efficiency have led to satisfying results in the reduction of consumption as well as in terms of technical losses, security increase and system reliability.
 Objective: The purpose of this article is to identify a method offering the best optimization outcome for the power flow of an energy distribution system with 10 nodes at 13.2 kV.
 Methodology: The results of voltage profiles are presented for a 10-node energy distribution system using the Newton Raphson method. Afterward, the system is optimized using genetic and ant colony algorithms.
 Results: Their implementation determined that the sum of the potential differences of distribution lines is notably reduced with the genetic algorithm. However, the ant colony optimization code takes less time to run and has a lower number of iterations.
 Conclusions: The most efficient optimization is achieved with the genetic algorithm since the evolution of the population shows better optimization levels in comparison to the ant colony algorithm.
 Financing: Universidad Francisco José de Caldas and Colciencias

Research on Signal-to-Noise Ratio Optimization based on Chaotic Immune Difference Algorithm

A new algorithm based on the combination of chaotic sequences and immune differencing is proposed to improve the population diversity capability of the immune differencing algorithm in order to improve the conventional immune differencing algorithm for solving the signal-to-noise optimal problem. The method uses chaotic sequence immune differencing for population expansion, and the accuracy of the algorithm is analysed. In order to test the effectiveness of the algorithm, it is applied to a function optimisation problem. Simulation results show that the method has a lower number of iterations and better accuracy.

Autonomous path planning through application of rotated two-parameter overrelaxation 9-point Laplacian iteration technique

Autonomous path navigation is one of the important studies in robotics since a robot’s ability to navigate through an environment brings about many advancements with it. This paper suggests the iteration technique called half-sweep two parameter overrelaxation 9-point laplacian (HSTOR-9P) to be applied on autonomous path navigation and aims to investigate its effectiveness in performing computation for path planning in an indoor static environment. This iteration technique is a harmonic function that solves the Laplace’s equation where the modelling of the environment is based on. The harmonic functions are an appropriate method to be used on autonomous path planning because it satisfies the min-max principle, therefore avoiding the occurrence of local minima which traps robot’s movements, and that it offers complete path planning algorithm. Its performance is tested against its predecessor iteration technique. Results shown that HSTOR-9P iteration technique enables path construction in a lower number of iterations, thus, performs better than its predecessors.

Hybrid Iterative Detection and Decoding of Near-Instantaneously Adaptive Turbo-Coded Sparse Code Multiple Access

Reduced-complexity hybrid detection and decoding (HDD) schemes are conceived for turbo-coded sparse code multiple access (SCMA) by analysing its convergence behavior using extrinsic information transfer (EXIT) charts, which outperforms the state of the art joint detector and decoder (JDD). As a benefit of its carefully controlled complexity, the resultant system is eminently suitable for ultra-reliable low-latency communication (URLLC) in multiuser scenarios, since it requires a low number of turbo iterations between the detector and the decoder. In particular, a pair of HDD schemes are proposed. HDD-I and HDD-II achieve a complexity reduction of up to 25% and 36%, respectively, at a similar bit error rate (BER) performance as that of JDD. Additionally, we propose an adaptive turbo-coded SCMA system for mitigating the influence of multipath propagation so that the system's bits per symbol (BPS) throughput may be improved under favorable channel conditions by using the most appropriate near-instantaneous user load, modulation order and coding rate. Our adaptive system design principle can also be readily used for other channel coding schemes.

Optimal tuning of a proportional controller for DC motor position control via Fibonacci Search Method

One of the advantages of using DC motors is the ease of controlling their position and speed by manipulating the input voltage. To this, feedback PID-based controllers can be used. However, adjusting these controllers can be challenging and require some reasonable effort from the controller designer. This work proposes an algorithm based on the Fibonacci Search Method to determine the optimal gain of a proportional controller applied to the position control of a DC motor. The project specifications were minimum settling time with null overshoot. The results obtained showed that the proposed method is valid for determining the Kp gain according to the plant and the conditions involved. The proposed method was compared with other optimization techniques such as Golden Section, Quasi-Newton and Grey Wolf Optimization, standing out for its simplicity of implementation, low number of iterations and fast convergence.