Iteration Step Research Articles

A greedy average block sparse Kaczmarz method for sparse solutions of linear systems

A greedy average block sparse Kaczmarz method is developed for the sparse solution of the linear system of equations. The convergence theory of this method is established and the upper bound of its convergence rate with adaptive stepsize is derived. Numerical experiments are presented to verify the efficiency of the proposed method, which outperforms the existing sparse Kaczmarz methods in terms of the number of iteration steps and computational costs.

Variational implicit solvation with Legendre-transformed Poisson–Boltzmann electrostatics

The variational implicit-solvent model (VISM) is an efficient approach to biomolecular interactions, where electrostatic interactions are crucial. The total VISM free energy of a dielectric boundary (i.e. solute–solvent interface) consists of the interfacial energy, solute–solvent interaction energy and dielectric electrostatic energy. The last part is the maximum value of the classical and concave Poisson–Boltzmann (PB) energy functional of electrostatic potentials, with the maximizer being the equilibrium electrostatic potential governed by the PB equation. For the consistency of energy minimization and computational stability, here we propose alternatively to minimize the convex Legendre-transformed Poisson–Boltzmann (LTPB) electrostatic energy functional of all dielectric displacements constrained by Gauss’ Law in the solute region. Both integrable and discrete solute charge densities are treated, and the duality of the LTPB and PB functionals is established. A penalty method is designed for the constrained minimization of the LTPB functional. In application to biomolecular interactions, we minimize the total VISM free energy iteratively, while in each step of such iteration, minimize the LTPB energy. Convergence of such a min–min algorithm is shown. Our numerical results on the solvation of a single ion indicate that the LTPB performs better than the PB formulation, providing possibilities for efficient biomolecular simulations.

A 2D path-planning performance comparison of RRT and RRT* for unmanned ground vehicle

In recent years, path-planning has gained significant attention as mobile robots are used in various applications. Several factors determine the optimal path for a mobile robot, including accuracy, length of path, execution time, and turns. Among all planners, sampling-based planners such as rapidly exploring random trees (RRT) and rapidly exploring random trees-star (RRT*) are extensively used for mobile robots. The aim of this paper is the review and performance of these planners in terms of step size, execution time, and path length. All planners are implemented on the Jackal robot in a static environment cluttered with obstacles. Performance comparisons have shown that the reduction of step size results in exploring a greater number of nodes in both algorithms, increasing the probability of each extension succeeding. However, this causes the tree to become denser in both algorithms due to the more explored nodes. The RRT planner requires less execution time when the step size and iteration count are equal to RRT* planners. Moreover, performance plots of both algorithms show that RRT* provides an optimal and smooth path than RRT.

Parameters optimization for chatter-free milling tool path based on spindle speed variation method

Existing methods orienting for optimizing cutting parameters in milling process with spindle speed variation (SSV) only optimally selected the axial depth of cut and only considered the acceleration constraint of spindle. This article proposes a systematic optimization method to obtain the best combination of multiple machining parameters (axial and radial depths of cut) and SSV’s parameters (the ratio of variation amplitude and the ratio of variation frequency) so that the chatter-free milling tool path can be shortened. Both acceleration and jerk constraints of spindle are considered. A C3 continuous variable feedrate scheduling algorithm is used to eliminate the influence of spindle speed variation on feed per tooth, which is thus not required to treat as an optimization variable. The machining parameters of each iterative step are updated by following that the number of axial and radial cutting layers could be decreased. Discrete optimization method is combined with chatter stability analysis to solve the problem. A series of milling experiments verify that the proposed method can achieve the shortest tool path with chatter-free condition.

An improved deep Q-network approach for charging sequence scheduling with optimal mobile charging cost and charging efficiency in wireless rechargeable sensor networks

Mobile charging is a practical solution to overcome energy constraints in wireless rechargeable sensor networks (WRSNs). It utilizes a mobile charger (MC) to charge sensors based on the wireless power transfer technique via a charging scheduling scheme. However, how to schedule MC to achieve higher charging efficiency with less mobile charging cost, while ensuring network functionality remains a challenge. Specifically, the mobile charging cost is the total energy consumed by the MC during the charging task. This paper studies the charging sequence scheduling problem with optimal mobile charging cost and charging efficiency (CSCE), considering dynamic changes in sensor energy consumption. Moreover, we model the CSCE as a multi-objective mixed integer nonlinear programming. To address this issue, we propose an improved deep Q network approach for CSCE (IDQN-CSCE), where the MC acts as an agent to explore the WRSN and determine a charging policy based on the charging demand of sensors. IDQN-CSCE employs Q-learning and deep Q-networks (DQN) to train the Q-table and DQN network simultaneously. Charging actions are selected based on probability, and the probability of using Q-learning decreases as iteration steps increase. Additionally, we design a novel reward function consisting of a weighted sum of rewards and penalties fed back by the environment after performing a charging action. Simulation results show that IDQN-CSCE achieves higher charging efficiency than other baseline approaches with less mobile charging cost.

WHOOPH: whale optimization-based optimal placement of hub node within a WBAN

Biosensor nodes of a wireless body area network (WBAN) transmit physiological parameter data to a central hub node, spending a substantial portion of their energy. Therefore, it is crucial to determine an optimal location for hub placement to minimize node energy consumption in data transmission. Existing methods determine the optimal hub location by sequentially placing the hub at multiple random locations within the WBAN. Performance measures like link reliability or overall node energy consumption in data transmission are estimated for each hub location. The best-performing location is finally selected for hub placement. Such methods are time-consuming. Moreover, the involvement of other nodes in the process of hub placement results in an undesirable loss of network energy. This paper shows the whale optimization algorithm (WOA)-based hub placement scheme. This scheme directly gives the best location for the hub in the least amount of time and with the least amount of help from other nodes. The presented scheme incorporates a population of candidate solutions called "whale search agents". These agents carry out the iterative steps of encircling the prey (identifying the best candidate solution), bubble-net feeding (exploitation phase), and random prey search (exploration phase). The WOA-based model eventually converges into an optimized solution that determines the optimal location for hub placement. The resultant hub location minimizes the overall amount of energy consumed by the WBAN nodes for data transmission, which ultimately results in an elongated lifespan of WBAN operation. The results show that the proposed WOA-based hub placement scheme outperforms various state-of-the-art related WBAN protocols by achieving a network lifetime of 8937 data transmission rounds with 93.8% network throughput and 9.74 ms network latency.

How do tour guides cope with knowledgeable tourists? Conceptualising knowledge/information asymmetry in tour-guiding contexts

Purpose Tourists’ resource integration both offers opportunities and presents challenges to tourism service providers. Focussing on the tour guide perspective, the purpose of this paper is to explore how tour guides experience knowledge/information-based asymmetry in encounters with tourists and identifies the roles and coping strategies used by guides to facilitate service co-production. Design/methodology/approach Critical incident technique is used in qualitative interviews with 47 tour guides in Scotland, broadly representative of the Scottish tour guiding context. 107 critical incidents were analysed, with an average of 2.32 incidents per interview. Narrative analysis of the incidents was performed inductively in four iterative steps using QSR NVivo. Findings Three resource asymmetry incident categories are identified: probing – Guide-Oracle is questioned by inquiring tourists and copes through diverting, evasion, and follow-up strategies; learning – Guide-Magpie learns from expert tourists through acknowledging and co-delivery; and negotiation – Guide-Diplomat with greater knowledge helps misguided tourists save face through appeasing, following the official line and tactfully correcting. Originality/value The paper contributes to service co-production research in tourism by theorising about contexts where knowledge/information asymmetry exists between tour guides and tourists, particularly where fluid power relations between guides and knowledgeable tourists occur or where misguided tourists co-produce the service by prioritising own meanings. Findings highlight the importance of soft skills and other non-content capabilities of guides, and suggestions are offered for effective training and resource sharing/ learning initiatives for tour guiding services.

The Helmholtz Equation with Uncertainties in the Wavenumber

We investigate the Helmholtz equation with suitable boundary conditions and uncertainties in the wavenumber. Thus the wavenumber is modeled as a random variable or a random field. We discretize the Helmholtz equation using finite differences in space, which leads to a linear system of algebraic equations including random variables. A stochastic Galerkin method yields a deterministic linear system of algebraic equations. This linear system is high-dimensional, sparse and complex symmetric but, in general, not hermitian. We therefore solve this system iteratively with GMRES and propose two preconditioners: a complex shifted Laplace preconditioner and a mean value preconditioner. Both preconditioners reduce the number of iteration steps as well as the computation time in our numerical experiments.

Exploring fathers’ experiences of caring for a child with complex care needs through ethnography and arts-based methodologies

BackgroundAlthough the number of children living with complex care needs (CCN) is increasing worldwide, there is limited data on the experience of fathers caring for children with CCN. This paper reports on findings specific to fathers’ experiences of caring for their child with CCN and highlights recommendations provided for parents of children with CCN, service providers, and policymakers. The findings emerged from a larger study designed to examine how Canadian families of children with CCN participate in society.MethodsWe used the qualitative research approach of ethnography and arts-based methodologies (ecomaps and photovoice) as well as purposive and snowball sampling techniques. Four parents were engaged as advisors and twenty-nine fathers participated in interviews (all were married or in a relationship; age range of 28 to 55 years). In line with an ethnographic approach, data analysis involved several iterative steps including comparing data from the first, second, and third set of interviews and refining themes.ResultsOne overarching theme, striving to be there for the child with CCN, was identified. Five supporting themes further exemplified how fathers strived to be there for their child: 1) contributing to the parental team through various roles; 2) building accessibility through adaptation; 3) engaging in activities with the child; 4) expressing admiration and pride in their children; and 5) meaning making. Recommendations for parents included making and nurturing connections and asking for help while recommendations for healthcare and social service providers included communicating authentically with families and listening to parents. Fathers also indicated that leadership and funding for programs of families of children with CCN should be priorities for policymakers.ConclusionsIn addition to documenting fathers’ active involvement in their child’s care and development, our findings provide new insights into how fathers make participation in everyday life accessible and inclusive for their children. Study findings also point to 1) priority areas for policymakers (e.g., accessible physical environments); 2) factors that are critical for fostering collaborative care teams with fathers; and 3) the need for complex care teams in the adult health care system. Implications for those providing psychosocial support for these families are noted as well as knowledge gaps worthy of future exploration such as the role of diversity or intersectionality in fathering children with CCN.

An accelerated lattice Boltzmann method for natural convection coupled with convolutional neural network

A novel approach is proposed in the present study, which combines the lattice Boltzmann method (LBM) with convolutional neural networks and is suitable for square cavity natural convection and conjugate natural convection problems, exhibiting remarkable acceleration capabilities and potentials. The density distribution function obtained from LBM at time t and the temperature distribution function at time t+Δt under different Rayleigh numbers (Ra) are, respectively, utilized as input and output datasets for training and comparison in three convolutional neural networks, aiming to select the optimal coupling model, namely, half-Res-Unet. The coupling model can accurately simulate the natural convection in a square cavity within six times the upper limit of the Ra under the training condition, which can save the central processing unit (CPU) calculation time and the iteration steps by up to 29.2% and 30.3%, respectively. The coupling model is further extended in the current study to incorporate conjugate natural convection, enabling the accurate simulation of temperature distribution under training conditions with a thermal conductivity ratio (Ka) of 25 and an upper limit of Ra increased by 20 times. The corresponding maximum relative errors for the average Nusselt numbers (Nu) are found to be 1.8% and 0.7%, respectively, providing strong evidence for the generalization capability of the coupling model. Furthermore, the coupling model demonstrates a remarkable acceleration performance, as evidenced by its ability to reduce the CPU calculation time by up to 39.6% and iteration steps in the simulation process by 36.5%. It offers valuable insights into the integration of LBM with machine learning techniques, thereby enhancing the computational efficiency of LBM.

Point convolutional neural network algorithm for Ising model ground state research based on spring vibration

The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising ground state search algorithm suitable for hardware computing is necessary for solving practical problems. Inspired by the potential energy conversion of the springs, we propose the Spring-Ising Algorithm, a point convolutional neural network algorithm for ground state search based on the spring vibration model. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establishes the equation of motion for all spins. Spring-Ising Algorithm can be mapped on AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has shown promising results in solving the Ising model and has been tested in the recognized test benchmark K2000. The optimal results of this algorithm after 10,000 steps of iteration are 2.9% of all results. The algorithm introduces the concept of dynamic equilibrium to achieve a more detailed local search by dynamically adjusting the weight of the Ising model in the spring oscillation model. Spring-Ising Algorithm offers the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations.

A multi-glimpse deep learning architecture to estimate socioeconomic census metrics in the context of extreme scope variance

Convolutional Neural Networks (CNNs) are leveraged for a wide range of satellite imagery information extraction tasks. However, for tasks which seek to estimate aggregated information across highly variable geographic extents, existing techniques are subject to critical limitations. We engage with a specific case study exploring this challenge: estimating census variables across 2358 Mexican municipalities, which range in scope from 2.21 km2 (˜74,000 30 m pixels) to 72,417.9 km2 (millions of pixels). Building on recent literature which has illustrated the capability of deep learning to extract socioeconomic information from satellite imagery, we specifically seek to establish baseline metrics of error that might be expected when estimating a range of census variables based on coarse-resolution (Landsat) satellite imagery alone. For each of 52 variables, we implement a multi-glimpse recurrent attention model, in which we parametrically determine subsets of each municipality to sample across iterative steps. Results of a five-fold validation indicate that nearly half of the tested variables (22) can be estimated with r2 values greater than 0.75. Results suggest considerable promise for the use of satellite imagery to estimate socioeconomic factors in both historic time periods for which surveys were not conducted, as well as contemporary inaccessible regions.

ЗМЕНШЕННЯ ВПЛИВУ ВІДХИЛЕНЬ ПАРАМЕТРІВ ГЕНЕРАТОРІВ У ПРЕЦИЗІЙНИХ КВАДРАТУРНИХ МОСТАХ

The article is devoted to solving the problem of attestation of electric capacity standards at industrial frequency. The expediency of using a quadrature bridge of alternating current to determine the parameters of standards of electric capacity at industrial frequency by comparison with the parameters of standards of active resistance was noted. The advantage of using a bridge imbalance indicator with high input impedance is shown, which consists in the possibility of reducing the influence of higher harmonics of the supply voltages. An analysis of the well-known method of reducing of measurement errors caused by the deviation of the generator parameters from the calculated ones was carried out. It is shown that the known method does not provide sufficient compensation for the influence of these errors when the deviations of the generator parameters increase, as well as when the deviations of the bridge from the balance state increase. An iterative algorithm for calculating the measured deviation of the impedance ratio of the compared standards from the nominal value is proposed. Mathematical expressions for calculating the measured quantity are given. Calculations of the components of the measurement error for different values of generator voltage deviations were carried out. Calculations were performed for two variants of generator voltage variation implementation: multiplicative voltage amplitude variation and additive voltage phase variation. The developed iterative correction method allows to reduce the specified errors to the required levels in a small number of iteration steps - in the vast majority of cases, two steps are enough. The application of the method allows obtaining high metrological characteristics with rather large deviations of the voltage ratio of bridge generators, which makes it possible to reduce hardware costs when implementing quadrature alternating current bridges for comparing capacitance and active resistance standards. References 15, figure 1, table 1.

A novel torque application method for tensegrity structures

The torsional resistance of the beam-like tensegrity structure is an essential mechanical property, but there is currently no systematic approach to applying torque to the structure using external force. This article presents a novel torque application strategy to analyze beam-like tensegrity structures. First, the torque that needs to be applied and the initial self-stable configuration are given. Second, the number of iteration steps is set, and the torque is gradually increased by using an arithmetic sequence, and the torque of each step is evenly distributed to the nodes of the acting surface. Third, the current external forces can be calculated by the nodal coordinates and torque, and the stable configuration under the obtained external force will be found based on the form-finding method. This process will be cycled until the specified torque is achieved. Finally, the equilibrium configuration under the set torque will be obtained, and the deformation analysis of the configuration will be carried out to study its mechanical properties. The elastic potential energy errors of the three-bar tensegrity prism unit and the class 2 tensegrity tower are corrected by 2.8198% and 66.9135%, respectively. This method has been proven to improve calculation precision.

Simulation of Pitting Corrosion Under Stress Based on Cellular Automata and Finite Element Method

Abstract A new cellular automaton (CA) program was written in python language to simulate the random pitting evolution process, which can not only obtain a variety of different corrosion products but also obtain a variety of common corrosion morphologies on the surface of metal pipes, bridge steel members, etc. In addition, commercial finite element (FE) software abaqus was redeveloped using python scripting language, and the FE mesh with the same size as the cellular mesh was established based on the consistent mesh algorithm, which ensured the efficiency and accuracy of the cyclic iterative algorithm. The stress and strain fields were calculated in real-time by applying the force load, the dissolution probability parameter P was updated in python according to the force-chemical coupling model, and a new corrosion morphology was obtained in python. At the same time, the birth and death element method was applied in abaqus to kill the corrosion elements in this iterative step simultaneously, and the new stress-strain field was recalculated in abaqus. The established consistent grid modeling strategy and cyclic iterative algorithm can significantly improve the solving efficiency of pitting evolution under the coupled action of corrosive medium and load. The results show that the stress concentration caused by pit expansion and the corrosion acceleration effect dominated by plastic deformation will promote each other, leading to the continuous growth of pitted pits. The established modeling strategy and cyclic iterative algorithm can significantly improve the solving efficiency of pitting evolution under the coupled action of corrosive medium and load.

An iterative method for updating finite element models with connectivity constraints

It is well known that the analytical matrices arising from the discretization of distributed parameter systems using the finite element technique are usually symmetric and banded. How to preserve the coefficient matrices of the updated model being of the same band structure is an important yet difficult challenge for model updating in structural dynamics. In this paper, an iterative method for updating mass, damping and stiffness matrices simultaneously based on partial modal measured data is provided. By the method, the optimal updated matrices can be obtained within finite iteration steps by choosing a special kind of initial matrix triplet. The proposed approach not only preserves the physical connectivity of the original model, but also the updated model reproduces the measured modal data, which can be utilized for various finite element model updating problems. Numerical examples confirm the effectiveness of the introduced method.

Alkene Hydrobenzylation by a Single Catalyst That Mediates Iterative Outer-Sphere Steps.

Cross-coupling catalysts typically react and unite functionally distinct partners via sequential inner-sphere elementary steps: coordination, migratory insertion, reductive elimination, etc. Here, we report a single catalyst that cross-couples styrenes and benzyl bromides via iterative outer-sphere steps: metal-ligand-carbon interactions. Each partner forms a stabilized radical intermediate, yet heterocoupled products predominate. The system is redox-neutral and, thus, avoids exogenous oxidants, resulting in simple and scalable conditions. Numerous variations of alkene hydrobenzylation are made possible, including access to the privileged heterodibenzyl (1,2-diarylethane) motif and challenging quaternary carbon variants.

Satellite actuator balancing based on the disturbance measurement table data

Reaction wheel is one of primary actuators for satellite attitude control. Since it uses electrical power supplied by the solar panels and thus consumes no invaluable fuel of satellite, it has been used as primary actuator. However, rotation of reaction wheel induces torque and force disturbances which degrade the satellite attitude stability quality. The disturbances are mainly due to static and dynamic imbalances of the wheel. These imbalances shall be reduced on the stage of manufacturing. In this his paper a method for the imbalance reduction of the wheel is suggested. Torque/force measurement table is used to measure the disturbances of the reaction wheel and the measured data are used for the identification of location and magnitude of the imbalances. The corrections of the imbalances are performed by adding proper counter masses on the wheel. Through iterative balancing steps, the static and dynamic imbalances are remarkably reduced.

The application of active filters supported by Pulse Width Modulated Inverters in the harmonic simulation of the high power electric traction

The paper shows the present results of a long term research work. The authors are working on the modelling of active filtering of harmonics caused by the railways. To simulate the harmonic penetration and filtering effects of the power electric traction the combination of frequency and time dependent model should be used. This novel method, called double domain simulation is improving the accuracy of the frequency domain simulation. To calculate the sophisticated model of the electric locomotive and active harmonic filter as a non-linear load a time dependent model must be used. The traction supply system together with the equivalent supply network impedance could be calculated in frequency domain. An iteration algorithm is developed converting the variables in every iteration step between the time and frequency domain.

Effect on power quality of the high power electric traction (Double domain computer simulation vs. site measurements).

The paper shows the present results of a long term research work. The authors are working on the modelling of penetration of harmonics caused by the railways. In a former paper [1] a detailed model of the traction system was discussed in frequency domain. However this paper presents the combination of frequency and time dependent model. This novel method, called double domain method is used to improve the accuracy of the frequency domain simulation. To calculate the sophisticated model of the electric locomotive as a non-linear load a time dependent model must be used. The traction supply system together with the equivalent supply network impedance could be calculated in frequency domain. An iteration algorithm is developed to converting the variables in every iteration step between the time and frequency domain.