Iteration Time Research Articles

Solving the periodic coupled operator matrix equations via BCR iterative method and its application in periodic state feedback pole assignment

PurposeThe paper aims to study the constraint solutions of the periodic coupled operator matrix equations by the biconjugate residual algorithm. The new algorithm can solve a lot of constraint solutions including Hamiltonian solutions and symmetric solutions, as special cases. At the end of this paper, the new algorithm is applied to the pole assignment problem.Design/methodology/approachWhen the studied periodic coupled operator matrix equations are consistent, it is proved that constraint solutions can converge to exact solutions. It is demonstrated that the solutions of the equations can be obtained by the new algorithm with any arbitrary initial matrices without rounding error in a finite number of iterative steps. In addition, the least norm-constrained solutions can also be calculated by selecting any initial matrices when the equations of the periodic coupled operator matrix are inconsistent.FindingsNumerical examples show that compared with some existing algorithms, the proposed method has higher convergence efficiency because less data are used in each iteration and the data is sufficient to complete an update. It not only has the best convergence accuracy but also requires the least running time for iteration, which greatly saves memory space.Originality/valueCompared with previous algorithms, the main feature of this algorithm is that it can synthesize these equations together to get a coupled operator matrix equation. Although the equation of this paper contains multiple submatrix equations, the algorithm in this paper only needs to use the information of one submatrix equation in the equation of this paper in each iteration so that different constraint solutions of different (coupled) matrix equations can be studied for this class of equations. However, previous articles need to iterate on a specific constraint solution of a matrix equation separately.

Facial Coding Study of the Effect of Halal Claims on Purchase Intention of Electronic Products: Linking Maslow's Hierarchy of Needs Theory

Usability testing plays a pivotal role in assessing user experience on websites, providing businesses with invaluable insights into customer perspectives. Eye-tracking, a widely accepted methodology, zeroes in on visual engagement by analyzing specific Areas of Interest (AOI). This study embarked on an exploration of the impact of AOI on dwell time for the latest iteration of the Waters website and further delineated the sections that captivated the most and least user attention. Adopting a quantitative stance, a two-way ANOVA was deployed on a sample comprising 15 master’s students from two distinct academic institutions. Preliminary findings underscore significant disparities in dwell times across different AOIs, implying that specific AOIs retain user attention more robustly. Notably, the research has its constraints, encompassing potential deviations in webcam-based eye-tracking accuracy, the modest sample size, and post-hoc AOI categorization. As an extension, subsequent studies could probe the nuanced factors swaying dwell time within Waters' website AOIs, integrating methodologies such as the WSD thnique and proactive AOI classification.

An efficient bandwidth-adaptive gradient compression algorithm for distributed training of deep neural networks

In distributed deep learning with data parallelism, communication bottleneck throttles the efficiency of model training. Recent studies adopt versatile gradient compression techniques, with communication sparsification standing out as an effective approach for reducing the number of gradients to be transmitted. However, the deployment of gradient sparsification is adversely influenced by the change of network environment in real systems, and existing methods either neglect bandwidth dynamics during training or experience drastic fluctuation of compression ratios. In this paper, we propose ACE, a novel adaptive gradient compression mechanism with high communication efficiency under bandwidth variation. ACE adapts the sparsification ratio to the average bandwidth in a time window, other than following its dynamics exactly. To accurately compute the compression ratio, we first profile the compression time and model a single iteration time consisting of communication, computation and compression operations. We then present a practical model to fit the needed training rounds till convergence, and formulate an optimization problem to compute the optimal sparsification ratio. We conduct experiments on different DNN models in different network environments and compare various methods in terms of convergence and model quality. The experimental results show that ACE achieves up to 9.39× and 1.28× training speedups over fixed and state-of-the-art adaptive compression methods.

Fractal-inspired multifrequency piezoelectric energy harvesters

In this Letter, we propose fractal-based piezoelectric energy harvesters (PEHs) for broadband energy scavenging. The introduction of fractal topology into transducers significantly alleviates the inherent limitation of a narrow working bandwidth in commonly used cantilever PEHs. We conduct a finite element analysis and experiments to exploit the performance of fractal cantilever PEHs with different iteration times. Our findings reveal that the higher-order fractal structures generate an increased number of eigenfrequencies as well as modal patterns within a certain range of working bandwidth (i.e., <50 Hz). Experimental results indicate that the efficient energy harvesting bandwidth of the fractal PEHs of iterative levels 1 and 2 is 2.05 and 2.15 times, respectively, larger than the conventional PEHs (i.e., level 0). In addition, the harvested voltage and power of fractal PEHs can be enhanced by attaching a proof mass to compensate for the energy loss in producing iterations. This method exhibits superiority over capturing energy in low-frequency vibration environments, such as wave energy and human movement energy.

Continuous ambulatory epilepsy detection system incorporating feature engineering

Epilepsy is a prevailing disease that affects people from different age brackets and demographic backgrounds. It leads to uncontrollable onset of seizures and can result in severe neurological injuries. In this paper, we devised a novel seizure prediction system as a real-time early warning system for patients. By using real-time transmissible, portable, and wireless devices, we can acquire raw data from scalp electroencephalogram (EEG) without any pre-processing for the input. After pre-processing, the data is fed into selected prediction algorithms based on literature review and a combination of methodologies. After times of iteration, our result shows a promising performance, with an accuracy rate of 100% Bonn dataset. We further designed a hardware data acquisition apparatus (with our program built-in) to smooth and ameliorate the data acquisition process when eliminating overmuch electrodes, which may serve as a promising seizure onset detecting device in the new era.

Experimental study of the effect of different 3D printing parameters on tensile strength, using artificial neural network

Numerous research studies have been conducted to optimize printing parameters using the fused deposition modeling technique (FDM) to improve mechanical properties. The large number of process parameters creates a need to search for optimal combinations of parameters to improve mechanical properties. This study examines the effects of three parameters when printing 3D with melted filament of a PLA material (Polylactic Acid) on the ultimate tensile strength of the printed parts. This search combines an experimental study of the most influential printing parameters on the tensile strength property, namely layer thickness, printing temperature, and feed rate. The experimental results are then analyzed and modeled as a linear regression model. Then develop an intelligent artificial model based on ANN (Artificial Neural Network) derived from these experimental results capable of predicting the optimal combination of parameters providing maximum tensile strength. The observed results showed that the feed rate dominates among the other variables, followed by the thickness of the layer. Also, at the level of prediction, the artificial model provides a better prediction of the tensile strength with a value of 36.1625 MPa by combining the following parameters: Feed rate: 70 mm s−1, temperature: 200 °C, and layer thickness: 0.26 mm, compared to the prediction obtained by the linear regression model. Neural networks enable more accurate optimization of 3D process parameters, leading to an overall improvement in the quality of finished products. predictive models, significantly reducing the iteration time required to obtain optimal parameters. The quality of the data used to train neural networks is crucial.

Usability Testing of Waters Brand Promotion Website on University Students

<em>Usability testing plays a pivotal role in assessing user experience on websites, providing businesses with invaluable insights into customer perspectives. Eye-tracking, a widely accepted methodology, zeroes in on visual engagement by analyzing specific Areas of Interest (AOI). This study embarked on an exploration of the impact of AOI on dwell time for the latest iteration of the Waters website and further delineated the sections that captivated the most and least user attention. Adopting a quantitative stance, a two-way ANOVA was deployed on a sample comprising 15 master’s students from two distinct academic institutions. Preliminary findings underscore significant disparities in dwell times across different AOIs, implying that specific AOIs retain user attention more robustly. Notably, the research has its constraints, encompassing potential deviations in webcam-based eye-tracking accuracy, the modest sample size, and post-hoc AOI categorization. As an extension, subsequent studies could probe the nuanced factors swaying dwell time within Waters' website AOIs, integrating methodologies such as the WSD technique and proactive AOI classification.</em>

A Class of Sixth-Order Iterative Methods for Solving Nonlinear Systems: The Convergence and Fractals of Attractive Basins

In this paper, a Newton-type iterative scheme for solving nonlinear systems is designed. In the process of proving the convergence order, we use the higher derivatives of the function and show that the convergence order of this iterative method is six. In order to avoid the influence of the existence of higher derivatives on the proof of convergence, we mainly discuss the convergence of this iterative method under weak conditions. In Banach space, the local convergence of the iterative scheme is established by using the ω-continuity condition of the first-order Fréchet derivative, and the application range of the iterative method is extended. In addition, we also give the radius of a convergence sphere and the uniqueness of its solution. Finally, the superiority of the new iterative method is illustrated by drawing attractive basins and comparing them with the average iterative times of other same-order iterative methods. Additionally, we utilize this iterative method to solve both nonlinear systems and nonlinear matrix sign functions. The applicability of this study is demonstrated by solving practical chemical problems.

Optimization of multi-vehicle obstacle avoidance based on improved artificial potential field method with PID control

In the context of multi-vehicle formation, obstacle avoidance in unknown environments presents a number of challenges, including obstacles near the target, susceptibility to local minima, and dynamic obstacle avoidance. To address these issues in multi-vehicle formation control and obstacle avoidance within unknown environments, this paper uses PID control to optimize the potential field function of the artificial potential field method and conducts simulation experiments. The results demonstrate that the proposed algorithm achieves reductions of 39.7%, 41.9%, 24.8% and 32.0% in four efficiency functions (total iteration times, formation efficiency function value, energy consumption and standard deviation of iteration times) compared to other algorithms. The improved algorithm more effectively addresses the challenge of slow obstacle avoidance when vehicles approach the target and can handle unexpected situations such as local minima and dynamic obstacles. It achieves energy-efficient optimization for multi-vehicle obstacle avoidance in complex environments.

Enhancing Urban Landscape Design: A GAN-Based Approach for Rapid Color Rendering of Park Sketches

In urban ecological development, the effective planning and design of living spaces are crucial. Traditional color plan rendering methods, mainly using generative adversarial networks (GANs), rely heavily on edge extraction. This often leads to the loss of important details from hand-drawn drafts, significantly affecting the portrayal of the designer’s key concepts. This issue is especially critical in complex park planning. To address this, our study introduces a system based on conditional GANs. This system rapidly converts black-and-white park sketches into comprehensive color designs. We also employ a data augmentation strategy to enhance the quality of the output. The research reveals: (1) Our model efficiently produces designs suitable for industrial applications. (2) The GAN-based data augmentation improves the data volume, leading to enhanced rendering effects. (3) Our unique approach of direct rendering from sketches offers a novel method in urban planning and design. This study aims to enhance the rendering aspect of an intelligent workflow for landscape design. More efficient rendering techniques will reduce the iteration time of early design solutions and promote the iterative speed of designers’ thinking, thus improving the speed and efficiency of the whole design process.

Distributed asynchronous measurement system fusion estimation based on inverse covariance intersection algorithm

For state estimation of multi-source asynchronous measurement systems with measurement missing phenomena, this paper proposes a distributed sequential inverse covariance intersection (DSICI) fusion algorithm based on conditional Kalman filtering method. It is mainly divided into synchronized state space module, local filtering module and fusion estimation module. The missing measurements occurring in the system are modelled and described by a set of random variables obeying a Bernoulli distribution. The synchronized state space module uses a state iteration method to synchronize the asynchronous measurement system at the moment of measurement update and it ensures the integrity of the measurement information. The local filtering module uses a conditional Kalman filtering algorithm for filter estimation. The reliability of the local filtering results is guaranteed because the local estimator designs a method to interact information with the domain sensors. The fusion estimation module designs a DSICI fusion algorithm with higher accuracy and satisfying consistency, which fuses the filtering results provided by each sensor when the relevant information between multiple sensors is unknown. Simulation examples demonstrate the excellent performance of the proposed algorithm, with a 33% improvement in accuracy over existing algorithms and an iteration time of less than 3 ms.

Speeding up large-wind-farm layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout

Abstract. As the use of wind energy expands worldwide, the wind energy industry is considering building larger clusters of turbines. Existing computational methods to design and optimize the layout of wind farms are well suited for medium-sized plants; however, these approaches need to be improved to ensure efficient scaling to large wind farms. This work investigates strategies for covering this gap, focusing on gradient-based (GB) approaches. We investigated the main bottlenecks of the problem, including the computational time per iteration, multi-start for GB optimization, and the number of iterations to achieve convergence. The open-source tools PyWake and TOPFARM were used to carry out the numerical experiments. The results show algorithmic differentiation (AD) as an effective strategy for reducing the time per iteration. The speedup reached by AD scales linearly with the number of wind turbines, reaching 75 times for a wind farm with 500 wind turbines. However, memory requirements may make AD unfeasible on personal computers or for larger farms. Moreover, flow case parallelization was found to reduce the time per iteration, but the speedup remains roughly constant with the number of wind turbines. Therefore, top-level parallelization of each multi-start was found to be a more efficient approach for GB optimization. The handling of spacing constraints was found to dominate the iteration time for large wind farms. In this study, we ran the optimizations without spacing constraints and observed that all wind turbines were separated by at least 1.4 D. The number of iterations until convergence was found to scale linearly with the number of wind turbines by a factor of 2.3, but further investigation is necessary for generalizations. Furthermore, we have found that initializing the layouts using a heuristic approach called Smart-Start (SMAST) significantly reduced the number of multi-starts during GB optimization. Running only one optimization for a wind farm with 279 turbines initialized with SMAST resulted in a higher final annual energy production (AEP) than 5000 optimizations initialized with random layouts. Finally, estimates for the total time reduction were made assuming that the trends found in this work for the time per iteration, number of iterations, and number of multi-starts hold for larger wind farms. One optimization of a wind farm with 500 wind turbines combining SMAST, AD, and flow case parallelization and without spacing constraints takes 15.6 h, whereas 5000 optimizations with random initial layouts, finite differences, spacing constraints, and top-level parallelization are expected to take around 300 years.

A fast closed-form approximate iterative fitting algorithm based on laser absorption spectrum

This paper presents a novel approach—an efficient closed-form approximation iterative fitting algorithm based on laser absorption spectra. Through this closed-form approximation iterative fitting, key parameters such as peak value, spectral line width, and normalized signal area serve as indicators for iteration completion, improving the speed without compromising accuracy. Furthermore, it employs the spectral signal of n cycles as a window for further processing, minimizing external interference. The results show that the proposed method averages 9.75 iterations, while the Levenberg–Marquardt fitting method averages 60.17 iterations. The average iteration time for the proposed method is 588.83 ms, a substantial 81.7% reduction compared to the 3210.5 ms required by the Levenberg–Marquardt fitting. These results decisively demonstrate the efficacy of the proposed method in reducing iteration time and enhancing measurement precision.

Robust adaptive iterative learning control for high-speed trains under non-strictly repeated conditions

In this paper, the speed and position control of high-speed trains (HSTs) under non-strictly repeated conditions is investigated. Considering the modeling accuracy and control cost, a multi-particle dynamics model is established in a single coordinate system. Based on this model, a novel robust adaptive iterative learning control (RAILC) method is proposed. The Lyapunov function is established to prove that the system state converges in a finite time, and the composite energy function (CEF) is constructed to prove that the control error approaches zero along the iteration axis. The application value in engineering practice of the proposed RAILC is verified by two simulation tests. Existing adaptive iterative learning control (AILC) and adaptive terminal sliding mode control (ATSMC) are introduced as control groups to show the system convergence of the RAILC on the iteration axis and time axis.

Modeling dynamic cracks initiation and propagation with the local to global approach

AbstractThe recently proposed local to global (L2G) method shows an excellent ability of modeling complex arbitrary cracking problem with high efficiency and robustness. Here, we extend the L2G to model dynamic cracks initiation and propagation, in which additional nodes are dynamically introduced to capture displacement discontinuities induced by cracks' propagation, but displacements/accelerations of additional nodes are obtained by solving local nonlinear problems of cracked elements associated with a crack. This unique operation removes the divergence risks caused by adding nodes, and improves the numerical efficiency by decreasing iteration times of the global problem. Crack branching problem is also taken into consideration in current work. Numerical examples for dynamic cracks propagation are provided to demonstrate the effectiveness and robustness of the proposed method.

Monte Carlo random walk simulation of transient absorption kinetics using reflectance and absorption of electrons at Au/TiO2 nanoparticle boundaries

To understand plasmon-induced charge-transfer mechanisms between a photo-excited gold (Au) nanoparticle and a TiO2 nanoparticle, a Monte Carlo random walk (MCRW) simulation was applied to reproduce the charge recombination kinetics in the nanocrystalline (Au/TiO[Formula: see text] assemblies reported previously based on transient absorption spectroscopy. The Au/TiO2 assemblies consist of a confined electron diffusion space within a tiny TiO2 nanoparticle, making it possible to study electron diffusion transport through MCRW simulation. In this simulation algorithm, the electron diffusion starts at the coordinate origin of a rectangle, and the next direction of movement is obtained by calculating the coordinate matrix and random offset so that the electron is reflected on three boundaries and absorbed when it reaches the other boundary. By simulation programming, the histogram which indicates the occurrence frequency of the step accumulation number up to the right boundary was obtained. From 100 to 100000 steps under condition of 10000 iteration, that is, changing the steps but keeping the iteration times to ensure that all particles experience absorption in the simulation. Comparing the trace of 106 particles position with that of 104 under 1000 simulations, the electron density was found to saturate other than the region near the right boundary, where electrons disappear by the absorption process during the electron diffusion process. Finally, by fitting curves, it is confirmed that the tendency of the simulated response reproduced the transient absorption kinetics.

A product performance rapid simulation approach driven by digital twin data: Part 1. For variable product structures

In the product design stage, the effective performance analysis method is the premise to guarantee the application performance of products. However, digital simulation, as a widely used performance analysis method, has a long simulation cycle and high requirements for computer hardware performance, which leads to long product design and iteration time. Facing this problem, a product performance rapid simulation approach driven by digital twin (DT) data for variable product structures is proposed to replace the digital simulation method. Firstly, the framework for variable product structures is designed, which includes a digital simulation module, a data-driven module, and a surrogate-based simulation module. Then, module implementation of the proposed framework is introduced in detail, in which the twin data-driven product performance rapid simulation model (RSM) for variable product structures is studied based on the makeTwin reference architecture. Finally, a case study is conducted on the iterative design of a connecting rod, focusing on equivalent stress evaluation. The result shows that the proposed method is feasible, as well as costs a shorter simulation time than traditional digital simulation.

Data Set Formation Method for Checking the Quality of Learning Language Models of the Transitive Relation in the Logical Conclusion Problem Context

A method for data set formation has been developed to verify the ability of pre-trained models to learn transitivity dependencies. The generated data set was used to test the quality of learning the transitivity dependencies in the task of natural language inference (NLI). Testing of a data set with a size of 10,000 samples (MultiNLI) used to test the RoBerta model. It was found that this model is good at studying transitive dependencies in the task of logical inference because all samples from the formed dataset were correctly classified as belonging to the class similar, contradiction and neutral. It was also investigated that in the task of logical inference, the class similarity is more directed than contradiction and neutral. Because if the premise and hypothesis in the data set are swapped, the accuracy of the RoBerta model decreases by a factor of 2.97,1.17,1.26 for the similar (0.98→0.33) , neutral (0.90→0.77) , and contradiction (0.98→0.78) classes, respectively. The study iteration time is 0.0028 seconds, so only half of the data set requires approximately 84 hours of collection. This research is relevant because the ability of natural language models to explore such dependencies as transitivity, which is not explicitly specified in the training data set, is an important element of the model’s ability to generalize. It was found that RoBerta’s model is good at studying transitive dependencies in the logical inference task because it correctly classified belonging to the class similar, contradiction, and neutral on all samples from the generated data set.

Enhancing solar heater performance: A comprehensive study on hybrid nanofluids and angled-rib turbulators for improved heat transfer and reduced irreversibility

This research investigates the performance of hybrid nanofluids in solar heating systems, focusing on heat transfer effectiveness and thermodynamic irreversibility. Solar energy is widely favored for its eco-friendly nature, but traditional operating fluids in solar heaters lack sufficient thermal conductivity. To address this issue, modified fluids called nanofluids have been developed. This study proposes a novel approach that combines nanofluids and turbulators to evaluate solar system performance. ANSYS FLUENT 18.0 solver is employed, utilizing the iterative time advancement technique for solving the set of equations. Entropy generation analysis is employed as a promising methodology. Aluminum-oxide nanoparticles are examined for their impact on heat transfer, pressure drop, and entropy generation in a rib channel at Re= 20,000. To enhance heat transfer, the effects of angled-ribs are assessed using the thermal enhancement factor, which considers heat transfer rate and friction factor. Results indicate that triangular-shaped ribs exhibit superior thermal performance. Furthermore, the study investigates the influence of nanofluid volume fraction and heat flux distribution on Nusselt number, pressure drop, thermal performance factor, and entropy generation. Increasing the nanofluid fraction enhances the Nusselt number, pressure drop, and performance evaluation criterion (PEC). Based on the results of a quantitative analysis, it can be concluded that 4% nanoparticle volume fraction results in an increase of 4.6%, 11.63%, and 18.5% in Nusselt number in triangular and rectangular channels without teeth and with teeth, respectively. Additionally, a positive heat flux gradient improves heat transfer and reduces entropy generation, suggesting that adjusting the boundary condition can enhance energy and exergy efficiency. Overall, this study demonstrates the benefits of entropy generation analysis for assessing thermal systems and implementing hybrid nanofluids with turbulators in solar heating, contributing valuable insights to sustainable energy technologies.

Three-Dimensional Iterative Enhancement for Coverage Hole Recovery in Underwater Wireless Sensor Networks

The efficient coverage of underwater wireless sensor networks (UWSNs) has become increasingly important because of the scarcity of underwater node resources. Complex underwater environments, water flow forces, and undulating seabed reduce the coverage effect of underwater nodes, even leading to coverage holes in UWSNs. To solve the problems of uneven coverage distribution and coverage holes, a three-dimensional iterative enhancement algorithm is proposed for UWSN coverage hole recovery using intelligent search followed by virtual force. Benefiting from biological heuristic search algorithms, improved particle swarm optimization is applied for node pre-coverage. With the change in iteration times, the adaptive inertia weight, acceleration factor, and node position are constantly updated. To avoid excessive coverage holes caused by search falling into local optimum, underwater nodes are considered as particles in the potential field whose virtual forces are calculated to guide nodes towards higher coverage positions. In addition, based on the optimal node location obtained by the proposed algorithm, the monitoring area is divided based on the clustering idea. The underwater routing protocol DBR based on depth information is subsequently used to optimize node residual energy, and its average is calculated comprehensively and compared with the other three coverage algorithms using the DBR routing protocol. Based on the experimental data, after 100 iterations, the coverage rates for BES, 3D-IVFA, DABVF, and the proposed algorithm are 83.28%, 88.85%, 89.31%, and 91.36%, respectively. Moreover, the proposed algorithm is further verified from the aspects of different node numbers, coverage efficiency, node movement trajectory, coverage hole, and average residual energy of nodes, which provides conditions for resource development and scientific research in marine environments.