Convergence Time Research Articles

Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: a comparative case study

Accurate reservoir characterization is necessary to effectively monitor, manage, and increase production. A seismic inversion methodology using a genetic algorithm (GA) and particle swarm optimization (PSO) technique is proposed in this study to characterize the reservoir both qualitatively and quantitatively. It is usually difficult and expensive to map deeper reservoirs in exploratory operations when using conventional approaches for reservoir characterization hence inversion based on advanced technique (GA and PSO) is proposed in this study. The main goal is to use GA and PSO to significantly lower the fitness (error) function between real seismic data and modeled synthetic data, which will allow us to estimate subsurface properties and accurately characterize the reservoir. Both techniques estimate subsurface properties in a comparable manner. Consequently, a qualitative and quantitative comparison is conducted between these two algorithms. Using two synthetic data and one real data from the Blackfoot field in Canada, the study examined subsurface acoustic impedance and porosity in the inter-well zone. Porosity and acoustic impedance are layer features, but seismic data is an interface property, hence these characteristics provide more useful and applicable reservoir information. The inverted results aid in the understanding of seismic data by providing incredibly high-resolution images of the subsurface. Both the GA and the PSO algorithms deliver outstanding results for both simulated and real data. The inverted section accurately delineated a high porosity zone (>20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>20\\%$$\\end{document}) that supported the high seismic amplitude anomaly by having a low acoustic impedance (6000–8500 m/s∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$*$$\\end{document} g/cc). This unusual zone is categorized as a reservoir (sand channel) and is located in the 1040–1065 ms time range. In this inversion process, after 400 iterations, the fitness error falls from 1 to 0.88 using GA optimization, compared to 1 to 0.25 using PSO. The convergence time for GA is 670,680 s, but the convergence time for PSO optimization is 356,400 s, showing that the former requires 88%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$88\\%$$\\end{document} more time than the latter.

Resisting technological solutionism in open universities in the time of global digital convergence

PurposeThis purpose of this paper is to explore the future of open universities (OUs), particularly in developing Asia, focusing on the potential risks associated with technological solutionism, or the popular belief that educational problems can be readily solved through technological means. This exploration is pertinent as the initial advantages that OUs once held have significantly diminished and competing higher education institutions worldwide are converging on a digital future.Design/methodology/approachThe qualitative method of empathy-based stories (MEBS) was employed as the primary research method for drawing data from sampled OU teachers. The obtained data were parsed via textual analysis and the lens of critical discourses on technology and higher education.FindingsThe study shows that, in the future envisioned by the sampled OU teachers, students’ lack of privileged access to frontier technologies is no barrier to learning success. It also emphasises that learning outcomes have causality beyond technological determinism, and that educational problems, which OUs may deem as requiring technological solutions, are often non-technological in nature and require no technological fixes.Originality/valueCritiques of techno-solutionism, such as the present study, are virtually absent in the context of OUs in developing Asia. For this very reason, this study is vital and serves as a guardrail while these OUs seek to reformulate their respective value propositions in the time of global digital convergence.

Tree detection and in-row localization for autonomous precision orchard management

This work presents a framework for localizing ground robots within fruit tree orchards. The standard practice of managing orchards at the large block-level does not maximize the potential of farms — individual plants have different needs due to variations in soil, pests, disease, irrigation, etc. In order to make selective management decisions for individual trees, such as precision fertilization, a robot must be able to accurately localize itself within the row. This is a challenge since in high density, modern orchard systems it is often difficult to obtain accurate GNSS measurements. Our algorithm begins by using deep learning to segment a tree trunk in an RGB-D image and then estimate its width. We then use the trunk segmentations and widths to calculate particle weights in a particle filter-based localization system. We show that integrating trunk width into the particle update step led to a 45% decrease in the distance traveled before convergence, and a 31% decrease in convergence time, alongside a marginal increase in the rate of correct convergence. We also demonstrate autonomous tree-level localization with a large ground robot in realistic field experiments in a commercial apple orchard.

Estimating observable-specific phase biases of GEO satellites from regional CORS to analyze its contribution in PPP ambiguity resolution

Achieving high-precision positioning and fast convergence has been the objective of Precision Point Positioning (PPP), which is restricted by the integer ambiguity resolution (AR). Recently, the emergence of observable specific code/phase bias (OSB) has gratefully alleviated this phenomenon. However, for the BeiDou Satellite Navigation System (BDS), only MEO/IGSO satellites are applied in BDS PPP-AR, while GEO satellites are generally ignored. It will largely limit the utilization in China or Asia-Pacific region, influencing both the convergence time and the positioning accuracy. To investigate the feasibility of applying GEO satellites for AR, the phase OSB of GEO satellites is estimated through the raw observations in this research. Based on the observations data from regional network in China region, the characteristics of BDS mixed-constellation OSBs are analyzed, and PPP-AR with different processing strategies in both static and kinematic mode are performed. The results indicate that the phase OSBs were relatively stable and the percentage of the estimated residuals for B1I/B3I signals within ±0.15/0.25 cycles were over 95/99% and 92/97% for IGSO and MEO satellites, while GEO satellites were slightly poor, with 89 and 96%, respectively. The average positioning accuracy of static PPP-AR containing GEO satellites was 0.55, 0.56 and 1.43 cm in the east, north and up directions, with improvements of 14.06, 3.44 and 9.48% respect to fixed solutions without GEO satellites. For kinematic mode, when the ambiguity of GEO satellites was solved, the convergence time and the average time to first fix were considerably reduced to 25.51 and 27.12 min, exhibiting the improvement of 26.16 and 14.80% compared to fixed solutions without GEO satellites, respectively. This further demonstrated the considerable applications potential of GEO satellites to regional users.

Speed‐sensorless control for SPMSM via novel exponential super‐twisting ADRC

AbstractAlthough the conventional super‐twisting reaching law (CSTRL) maintains the desired tracking performance for disturbed systems, its implementation is challenging due to its dependence on accurate system dynamics. To mitigate this dependence and reduce chattering of CSTRL, a novel exponential super‐twisting reaching law (NESTRL) is proposed, leveraging the dynamic adaptability of the exponential reaching law and eliminating chattering. By using NESTRL, the proposed controller can achieve a fast response without chattering. Additionally, a comprehensive procedure for finite convergence time analysis is provided. The convergence conditions of the system's state variables are determined using a Lyapunov function, and the stability of system is proven using an algebraic Lyapunov function. Finally, simulations and experimental results demonstrate that the proposed approach is valid and effective, showing improvements compared to the traditional super‐twisting active disturbance rejection control and linear active disturbance rejection control in sensorless control.

Enhancing MPPT efficiency in PV systems under partial shading: A hybrid POA&PO approach for rapid and accurate energy harvesting

Partial shading in operational environments introduces multiple peaks in the output characteristics of photovoltaic (PV) systems, presenting significant challenges to energy harvesting. This study introduces a novel meta-heuristic algorithm, termed POA&PO, which aims to address the maximum power point tracking (MPPT) issues in PV systems. The algorithm capitalizes on the global search capability of the POA method to quickly pinpoint the range with the maximum power, followed by the fast convergence of the PO method to ensure both rapidity and accuracy of the solution. Extensive simulation tests, conducted in MATLAB/SIMULINK, have demonstrated the efficacy of the POA&PO algorithm, achieving an average tracking efficiency of 99.97 % with a convergence time of 0.3 s in step response tests; under the EN50530 test standard, the algorithm also showed sustained and stable tracking of ramp signals. Moreover, practical testing utilizing a new, low-cost indoor PV simulator confirmed the algorithm’s high performance under controlled conditions, yielding an average tracking efficiency of 97.03 % and a convergence time of 0.18 s. This paper highlights the capacity of the developed algorithm to reliably, accurately, and swiftly achieve high energy transfer efficiency. Additionally, the innovative and economical experimental testing methods employed are emphasized, contributing to the practical applicability and cost-effectiveness of the proposed solution.

Scaling up magnetocaloric heat pump for building decarbonization initiatives

Building decarbonization necessitates renewable heating and cooling solutions such as heat pumps. Magnetocaloric heat pumps (MCHP) offer environmental and efficiency advantages but face challenges when scaling up from existing active magnetic regenerator configurations. This study highlights uneven flow resistance, porosity, and refrigerant magnetocaloric effects as key obstacles to MCHP performance in parallel multi-bed setups. To address these effects, two control strategies for the fluid flow control system were investigated: measurement feedback control and model predictive control. Results show a 36.9 % heating power improvement with measurement feedback control, though with extended control convergence times. Model predictive control achieved approximately seven times faster control convergence compared to the measurement feedback control strategy, despite exhibiting minor overshooting. Utilization factor-based model predictive control increased the heating capacity by 1.6 %–30.9 % and the COP by 1.2 %–10.7 % in scenarios with uneven flow resistance and porosity, offering computational efficiency but assuming even magnetocaloric effects between regenerators. This assumption can be addressed by outlet temperature-based model predictive control, albeit at a higher computational cost using genetic algorithm. The findings emphasize the importance of advanced control methods to scaling up MCHP in renewable energy building systems.

Analisis Penggunaan Metode XConnect Main and Backup Link Berbasis VPN MPLS Layer 2 pada Jaringan Metro-Ethernet

The rapid development of information and communication technology requires excellent scalability, which is crucial for network performance availability. MPLS is a technology that can be used for communication in high-speed backbone networks based on virtual private networks (VPN). MPLS provides solutions to various current computer network problems such as speed, scalability, quality of service (QoS), and network traffic management. This research aims to optimize the scalability of MPLS Layer 2 VPN-based network traffic by applying the xconnect main and backup method implemented in metro Ethernet networks. The network simulation implementation was created using the Emulated Virtual Environment – Next Generation (EVE-NG) application to obtain data on convergence time and QoS parameters according to TIPHON standards, as well as the use of MPLS and Route Reflector to enhance MPLS backbone performance. This research shows that both main and backup MPLS L2VPN paths have good index values in packet delivery according to TIPHON standards, with an average rating of 3.0. Thus, this research can help optimize path scalability to improve the quality of MPLS L2VPN network traffic.

Geometric extended state observer on [formula omitted] with fast finite-time stability: Theory and validation on a multi-rotor vehicle

This article presents an extended state observer for a vehicle modeled as a rigid body in three-dimensional translational and rotational motions. The extended state observer is applicable to a multi-rotor aerial vehicle with a fixed plane of rotors, modeled as an under-actuated system on the state-space TSE(3), the tangent bundle of the six-dimensional Lie group SE(3). This state-space representation globally represents rigid body motions without singularities. The extended state observer is designed to estimate the resultant external disturbance force and disturbance torque acting on the vehicle. It guarantees stable convergence of disturbance estimation errors in finite time when the disturbances are constant, and finite time convergence to a bounded neighborhood of zero errors for time-varying disturbances. This extended state observer design is based on a Hölder-continuous fast finite time stable differentiator that is similar to the super-twisting algorithm, to obtain fast convergence. Numerical simulations are conducted to validate the proposed extended state observer. The proposed extended state observer is compared with other existing research to show its advantages. A set of experimental results implementing disturbance rejection control using feedback of disturbance estimates from this extended state observer is also presented.

Simplified control algorithm for stable and efficient standalone PV systems: An assessment based on real climatic conditions

This research addresses the critical challenge of optimizing efficiency and performance in solar photovoltaic (PV) systems. It focuses on duty cycle optimization of DC/DC converters for maximum power point tracking (MPPT). The study introduces an innovative hybrid MPPT solution that integrates a novel temperature-based single-sensor approach with an integral backstepping controller. This method precisely achieves the PV array's maximum power point (MPP) under varying weather conditions while optimizing the converter's duty cycle to best align with the MPP voltage. Comprehensive simulations in MATLAB/Simulink reveal that the hybrid method surpasses conventional MPPT systems in key metrics, achieving faster convergence time (below 0.66 ms), reduced power ripple (<0.3 W), and enhanced tracking efficiency (exceeding 97.67 %). Notably, experimental data validates the practical efficacy of the proposed system under real-world conditions in the context of El Jadida, Morocco. This integrated approach advances the theoretical understanding of MPPT optimization and demonstrates tangible improvements in PV system performance, offering significant potential for enhancing solar energy utilization in variable climate regions.

A Methodology to Optimize PMSM Driven Solar Water Pumps Using a Hybrid MPPT Approach in Partially Shaded Conditions

Solar water pumps are crucial for farmers, significantly reducing energy costs and providing independence from conventional fuels. Their adoption is further incentivized by government subsidies, making them a practical choice that aligns with sustainable agricultural practices. However, the cost of the required solar panels for the chosen power makes it essential to optimize solar water pumping systems (SWPS) for economic viability. This study enhances the efficiency and reliability of permanent magnet synchronous motor (PMSM)-driven SWPS in rural areas using hybrid maximum power point tracking (MPPT) algorithms and voltage-to-frequency (V/f) control strategy. It investigates the sensorless scalar control method for PMSM-based water pumps and evaluates various MPPT algorithms, including grey wolf optimization (GWO), particle swarm optimization (PSO), perturb and observe (PO), and incremental conductance (INC), along with hybrid combinations. The study, conducted using MATLAB Simulink, assesses these algorithms on convergence time, MPPT accuracy, torque ripple, and system efficiency under different partial shading conditions. Findings reveal that INC-GWO excels, providing higher accuracy, faster convergence, and reduced steady-state oscillations, thus boosting system efficiency. The V/f control strategy simplifies control mechanisms and enhances performance. Considering system non-idealities and maximum duty cycle limitations, PMSM-based SWPS achieve superior efficiency and stability, making them viable for off-grid water pumping applications.

Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter

Machine learning, a rapidly growing field, has attracted numerous researchers for its ability to automatically learn from and make predictions based on data. This manuscript presents an innovative approach to estimating the covariance matrix of noise in radar measurements for target tracking, resulting from collaborative efforts. Traditionally, researchers have assumed that the covariance matrix of noise in sonar measurements is present in the vast majority of literature related to target tracking. On the other hand, this research aims to estimate it by employing deep learning algorithms with noisy measurements in range, bearing, and elevation from radar sensors. This collaborative approach, involving multiple disciplines, provides a more precise and accurate covariance matrix estimate. Additionally, the unscented Kalman filter was combined with the gated recurrent unit, multilayer perceptron, convolutional neural network, and long short-term memory to accomplish the task of 3D target tracking in an airborne environment. The quantification of the results was achieved through the use of Monte Carlo simulations, which demonstrated that the convolutional neural network performed better than any other approach. The system was simulated using a Python program, and the proposed method offers higher accuracy and faster convergence time than conventional target tracking methods. This is a demonstration of the potential that collaboration can have in research.

Using Entropy as the Convergence Criteria of Ant Colony Optimization and the Application at Gene Chip Data Analysis.

When Ant Colony Optimization algorithm (ACO) is adept at identifying the shortest path, the temporary solution is uncertain during the iterative process. All temporary solutions form a solution set. Where each solution is random. That is, the solution set has entropy. When the solution tends to be stable, the entropy also converges to a fixed value. Therefore, it was proposed in this paper that apply entropy as a convergence criterion of ACO. The advantage of the proposed criterion is that it approximates the optimal convergence time of the algorithm. In order to prove the superiority of the entropy convergence criterion, it was used to cluster gene chip data, which were sampled from patients of Alzheimer's Disease (AD). The clustering algorithm is compared with six typical clustering algorithms. The comparison shows that the ACO using entropy as a convergence criterion is of good quality. At the same time, applying the presented algorithm, we analyzed the clustering characteristics of genes related to energy metabolism and found that as AD occurs, the entropy of the energy metabolism system decreases; that is, the system disorder decreases significantly.

Automatic source code generation for deterministic global optimization with parallel architectures

Trends over the past two decades indicate that much of the performance gains of commercial optimization solvers is due to improvements in x86 hardware. To continue making progress, it is critical to consider alternative/specialized massively parallel computing architectures. In this work, we detail the development of an open-source source code transformation approach built using Symbolics.jl to construct McCormick-based relaxations of functions that enables their effective parallelized evaluation. We then apply this approach in a novel parallelized branch-and-bound routine that offloads lower- and upper-bounding problems to a GPU. The effectiveness of this new approach is demonstrated on three nonconvex problems of interest, where it yields convergence time improvements of 22–118x compared to an equivalent serial CPU implementation and in two cases outperforms vanilla branch-and-bound versions of existing state-of-the-art solvers that use tighter bounding techniques. This work exemplifies how deterministic global optimizers using alternative hardware architectures can compete with—or eventually outclass—even the most powerful serial CPU implementations, and to the best of the authors' knowledge, represents the first successful demonstration of deterministic global optimization using a GPU.

DDPG-AdaptConfig: A deep reinforcement learning framework for adaptive device selection and training configuration in heterogeneity federated learning

Federated Learning (FL) is a distributed machine learning approach that protects user privacy by collaboratively training shared models across devices without sharing their raw personal data. Despite its advantages, FL faces issues of increased convergence time and decreased accuracy due to the heterogeneity of data and systems across devices. Existing methods for solving these issues using reinforcement learning often ignore the adaptive configuration of local training hyperparameters to suit varying data characteristics and system resources. Moreover, they frequently overlook the heterogeneous information contained within local model parameters. To address these problems, we propose the DDPG-AdaptConfig framework based on Deep Deterministic Policy Gradient (DDPG) for adaptive device selection and local training hyperparameters configuration in FL to speed up convergence and ensure high model accuracy. Additionally, we develop a new actor network that integrates the transformer mechanism to extract heterogeneous information from model parameters, which assists in device selection and hyperparameters configuration. Furthermore, we introduce a clustering-based aggregation strategy to accommodate heterogeneity and prevent performance declines. Experimental results show that our DDPG-AdaptConfig achieves significant improvements over existing baselines.

Fixed-time active fault-tolerant control for dynamical systems with intermittent faults and unknown disturbances

In this article, the problem of fixed-time active fault-tolerant control is investigated for dynamical linear systems with intermittent faults and unknown disturbances. Unlike traditional active fault-tolerant control, fixed-time control is taken into account in this article since intermittent faults appear and disappear within a certain period of time. The entire active fault-tolerant control framework is composed of fault detection, fault isolation, fault and state estimation as well as the reconfigurable controller. Using the homogeneity-based observers, states and faults are well estimated and a fault diagnosis scheme is proposed for the sake of detecting and isolating intermittent faults in a fixed time. The fault-tolerant controller, which provides global practical fixed-time stability of the closed-loop system, has two switching states corresponding to the appearance and disappearance of intermittent faults. As a consequence, intermittent faults are compensated via the designed active fault-tolerant control method and the system reaches practical stability with the entire convergence time bounded in a fixed time. Finally, two examples are exploited to demonstrate the effectiveness of theoretical results.

Positivity preserving and unconditionally stable numerical scheme for the three-dimensional modified Fisher–Kolmogorov–Petrovsky–Piskunov equation

This paper introduces a numerical approach for the practical solution of the modified Fisher–Kolmogorov–Petrovsky–Piskunov equation that describes population dynamics. The diffusion term and nonlinear term is based on the operator splitting method and interpolation method, respectively. The analytic proof of the discrete maximum principle and positivity preserving for the numerical algorithm is demonstrated. Numerical solution calculated using the proposed method remains stable without blowing up, which implies that the proposed method is unconditionally stable. Numerical studies show that the proposed method is second-order convergence in space and first-order convergence in time. The performance and applicability of the proposed scheme are studied through various computational tests that present the effects of model parameters and evolution dynamics.

A self‐stabilizing distributed algorithm for the 1‐MIS problem under the distance‐3 model

SummaryFault‐tolerance and self‐organization are critical properties in modern distributed systems. Self‐stabilization is a class of fault‐tolerant distributed algorithms which has the ability to recover from any kind and any finite number of transient faults and topology changes. In this article, we propose a self‐stabilizing distributed algorithm for the 1‐MIS problem under the unfair central daemon assuming the distance‐3 model. Here, in the distance‐3 model, each process can refer to the values of local variables of processes within three hops. Intuitively speaking, the 1‐MIS problem is a variant of the maximal independent set (MIS) problem with improved local optimizations. The time complexity (convergence time) of our algorithm is steps and the space complexity is bits, where is the number of processes. Finally, we extend the notion of 1‐MIS to ‐MIS for each nonnegative integer , and compare the set sizes of ‐MIS () and the maximum independent set.

Compensation control of commercial vehicle platoon considering communication delay and response lag

The real-time accurate control of vehicle is of great significance for the stability of platoon driving. To address the impact of random time-varying communication delays, communication packet loss, data disorder, and significant response lag of commercial vehicle on platoon control performance in non-ideal communication environment, a novel compensation control method for commercial vehicle platoon is proposed to overcome the abovementioned issues. The proposed method significantly improves the impact of communication issues and vehicle response lag on platoon control, ensuring platoon safety while reducing inter-vehicle spacing and spacing error, and maintaining platoon stability. In addition, this method combines the comprehensive compensation mechanism with the Distributed Model Predictive Controller (DMPC), avoiding the establishment of additional compensation control modules and effectively reducing the computational burden. The results under emergency braking condition show that, compared with the method without compensation, the proposed method significantly reduces inter-vehicle spacing and spacing error, reducing the average maximum spacing error from 29.25 m to 1.44 m, shortening the error convergence time of the platoon by 28 %, and ensuring platoon stability. Additionally, compared to the compensation method based on Kalman Filtering (KF) and Smith predictor, the proposed method can reduce the average maximum spacing error by 60 % while maintaining similar convergence time and platoon stability.

Model‐free adaptive load frequency control for power systems with wind penetration under deregulation environment

AbstractWith the gradual deregulation of the power system by the power department, the power system has developed into a large‐scale and multiregional control system. Because of the power system internal complexity enhancing, the stable operation of power system becomes increasingly difficult. This paper analyzes the load frequency control problem of multiregional interconnected power system with wind energy. This study designs an improved model‐free adaptive control algorithm based on I/O data. It avoids model establishment of the multiregional power system. It also effectively solves the problem of frequency stability control under the influence of load change, introducing the generation participation matrix to simulate bilateral contracts under the power market. The dynamic evolution relationship of the system with the generation participation matrix is established, taking a three‐regional power system with wind energy as an example. Frequency fluctuations in all three regions are between . Convergence times of frequency deviation are all within 30 s, much less than the response time of load frequency control. The simulation results further demonstrate the effectiveness of the proposed algorithm, comparing the control algorithm proposed in this paper with other algorithms, which proves that the proposed algorithm has good control performance.