Parameter Control Strategy Research Articles

Collective behavior of active filaments with homogeneous and heterogeneous stiffness.

The collective dynamics of active biopolymers is crucial for many processes in life, such as cellular motility, intracellular transport, and division. Recent experiments revealed fascinating self-organized patterns of diverse active filaments, while an explicit parameter control strategy remains an open problem. Moreover, theoretical studies so far mostly dealt with active chains with uniform stiffness, which are inadequate in describing the more complicated class of polymers with varying stiffness along the backbone. Here, using Langevin dynamics simulations, we investigate the collective behavior of active chains with homogeneous and heterogeneous stiffness in a comparative manner. We map a detailed non-equilibrium phase diagram in activity and stiffness parameter space. A wide range of phase states, including melt, cluster, spiral, polar, and vortex, are demonstrated. The appropriate parameter combination for large-scale polar and vortex formation is identified. In addition, we find that stiffness heterogeneity can substantially modulate the phase behaviors of the system. It has an evident destructive effect on the long-ranged polar structure but benefits the stability of the vortex pattern. Intriguingly, we unravel a novel polar-vortex transition in both homogeneous and heterogeneous systems, which is closely related to the local alignment mechanism. Overall, we achieve new insights into how the interplay among activity, stiffness, and heterogeneity affects the collective dynamics of active filament systems.

Optimising Deep Neural Networks for Tumour Diagnosis Algorithms Based on Improved MRFO Algorithm

INTRODUCTION: Cancer has become one of the most prevalent diseases with the highest mortality rate in the world, and timely detection and early acceptance of medical therapeutic interventions are effective means of controlling the progression of cancer patients and improving their post-intervention outcomes.OBJECTIVES: To make the defects of incomplete features, low accuracy and low real-time performance of current tumour diagnosis methods.METHODS: This paper proposes a tumour diagnosis method based on the improved MRFO algorithm to improve the optimization process of DBN network parameters. Firstly, the diagnostic features are extracted by analysing the tumour diagnosis identification problem; then, the manta ray foraging optimization algorithm is improved by combining the good point set initialization strategy, the adaptive control parameter strategy and the distribution estimation strategy, and the tumour diagnostic model based on the improved manta ray foraging optimization algorithm to optimize the parameters of the depth confidence network is constructed; finally, the high accuracy and real-time performance of the proposed method are verified by the analysis of simulation experiments.RESULTS: The results show that the proposed method improves the accuracy of the diagnostic model.CONLUSION: Addresses the problem of poor accuracy and real-time availability of tumour diagnostic methods.

Three-Level DC-DC Boost Converter for Wind Energy Conversion

In recent years, advancements in power converter technology for wind energy conversion systems have aimed to enhance power output, increase reliability, and improve overall efficiency. However, achieving a fixed and high-power DC output remains a challenge. This study explores three modes of operation for a DC-DC boost converter to enhance fixed DC output, essential for various applications like battery charging and electronics. The research categorizes DC system topologies based on factors such as setup complexity, cost-effectiveness, and performance metrics. It proposes a DC/DC conversion approach that offers cost-effectiveness and efficiency, aiming to set a new standard in topology for future converters. The study addresses key challenges in the efficiency of the step-up process of DC/DC converters, focusing on parameter design, operational principles, and control strategies. Simulation results confirm the effectiveness of the suggested approach, emphasizing its future potential for non-isolated high step-up DC/DC converters. Reduced device voltage stress and increased efficiency represent the two advantages of the suggested three-level DC-DC boost converter, which combines a standard boost converter with a switched capacitor function to supply three capacitors in series. Simulation results demonstrate the converter’s effectiveness in a wind energy conversion system, showcasing its potential for high-power applications.

An adaptative differential evolution with enhanced diversity and restart mechanism

Differential Evolution (DE) stands out as an exceptional intelligent evolutionary algorithm, acclaimed for its simplicity in implementation and the ability to optimize without necessitating differentiable conditions. However, a significant pitfall of DE lies in its susceptibility to getting trapped in local minima, leading to algorithmic stagnation and substantial performance impairments. To counteract the shortcoming, the Adaptative Differential Evolution with Diversity Maintenance and Restart Mechanism (ADE-DMRM) is proposed. This approach integrates three primary innovations. Firstly, the algorithm controls the number of generations that individuals stay in the external archive based on the successful evolution rate, thereby optimizing the mutation strategy to effectively maintain population diversity. Secondly, ADE-DMRM proposes a novel restart mechanism in which the current stagnant individuals are identified by combining a stagnation tracker and a diversity assessment indicator and then regenerated using a dimension-learning-based approach. Thirdly, wavelet basis functions and Cauchy distributions are employed for scaling factor implementation across different stages, while the dimension change information of successfully evolved individuals is harnessed to refine the adaptive parameter control scheme. Finally, comprehensive experiments were undertaken on the CEC2013, CEC2014, and CEC2017 test sets, assessing accuracy, convergence speed, the efficacy of each module, and time complexity. The results confirm that ADE-DMRM is an efficient single-objective optimization algorithm that outperforms current advanced variants.

Parametric adaptive inverter VSG control strategy with transient damping characteristics

Parametric adaptive inverter VSG control strategy with transient damping characteristics

A Continuous Process for Manufacturing Apremilast. Part II: Process Characterization to Establish a Parametric Control Strategy

A Continuous Process for Manufacturing Apremilast. Part II: Process Characterization to Establish a Parametric Control Strategy

Double-layer prescribed performance control of free-floating space manipulators after capturing unknown targets

This paper studies the parameter identification and prescribed performance control of the free-floating space manipulator after capturing unknown targets. A novel finite-time concurrent learning (FT-CL) parameter identification algorithm utilizing suitable historical data is proposed. This algorithm guarantees the finite-time convergence of the parameter estimation error without using the persistent excitation condition. Then, we apply the dual modeling approach and view the end-effector states as generalized coordinates of novel virtual dual dynamics. This technique bypasses solving the inverse kinematics and calculating the Jacobi matrix, laying the foundation for Jacobi-free pose control of the end-effector. Besides, stabilizers of prescribed performance reference acceleration are carefully designed in a double-layer form for regulating end-effector pose and base attitude. Finally, the Jacobi-free controllers satisfying the double-layer performance constraints are developed. The efficacy of the proposed parameter identification scheme and control strategy is validated through numerical simulations.

Design Optimization of an Automotive Permanent-Magnet Synchronous Motor by Combining DOE and NMGWO

This study proposes an optimization methodology for automotive permanent-magnet synchronous motors (PMSMs) to achieve maximum efficiency, maximum average torque, and minimum torque ripple. Many geometrical parameters can be used to define the PMSM of an automobile. To identify the most significant parameters for optimization, the fractional factorial design of the design of experiment (DOE) was employed for screening, considering the interaction effects. The central composite design was used to construct the proxy model between the optimization target and optimization variable, and the effectiveness of the model was judged. Aiming at the multi-objective optimization problem of a motor, a new mechanism for grey wolf optimizer (NMGWO) algorithm combining an elite reverse learning strategy, a local search strategy, and a nonlinear control parameter strategy is innovatively proposed. This algorithm was applied to solve the multi-objective optimization model. The numerical calculation results show that this is an effective optimization design method that can improve the performance of automotive PMSMs. The effectiveness of the NMGWO algorithm on the optimization results of permanent-magnet synchronous motors is verified by the experimental results.

Adaptive Tikhonov regularization and dynamic control points for accurate shape parameter control of plasmas

Precise control of plasma shape parameters, such as elongation and triangularity is duly needed to achieve high-performance tokamak plasmas, for which we propose adaptive search schemes of (1) optimum regularization parameter for the Tikhonov regularization, and (2) control points to specify the key shape parameters such as elongation and triangularity. Many control points that exceed the number of actuator coils become an ill-conditioned problem, which is successfully resolved by Tikhonov regularization with adaptively optimized free parameters. Furthermore, we develop dynamically changed control points by using the Cauchy Condition Surface scheme, where elongation and triangularity become direct control values. By virtue of both the adaptive Tikhonov regularization scheme and the dynamic control point, we achieved accurate shape control with elongation up to 1.93 and triangularity up to 0.65 in JT-60SA, where the allowable speed for the change of the elongation is 0.1 s−1. We also verified the resilience of our developed our logics to the noises. The sequence of the result will contribute to enhance equilibrium controllability in upcoming JT-60SA experiments and provide the robust shape parameter control scheme for ITER and DEMO.

A Minimum Cost Design Approach for Steel Frames Based on a Parallelized Firefly Algorithm and Parameter Control

In this work, the applicability of a Firefly Algorithm (FA) to the real problem of the minimum cost of a detailed design for steel frames is studied. To reduce the calculation time, which is a common problem of meta-heuristic algorithms when they are used to solve real design cases, and to better suit the characteristics of the algorithm, a parallel migration strategy has been implemented and tested. As it is well known that the performance of any metaheuristic algorithm depends on the chosen value of its parameters, an extensive sensitivity analysis has been carried out. This not only serves to improve performance but also provides information on how it depends on the values of these parameters. With the information obtained from this analysis, and in order to achieve the robust behavior of the algorithm, a parameter control strategy has also been implemented and tested. Finally, a study demonstrating the close dependence between one of the parameters and the number of variables considered in the examples has been carried out. As a result of this final study, a simple expression is proposed that provides the minimum necessary population based on the number of variables in the problem.

Numerical analysis of a solid oxide fuel cell system integrated with a hybrid desiccant cooling system

Solid oxide fuel cell systems are advantageous in distributed power generation and are commonly utilized in residential buildings. However, since commercialized small-scale solid oxide fuel cells generally produce hot water using high-temperature exhaust gas, it is difficult to utilize thermal energy in high-temperature areas and during the summer. The exhaust heat from the solid oxide fuel cell system can be effectively utilized by integration with the hybrid desiccant cooling system. In this study, a numerical model of a solid oxide fuel cell system integrated with a hybrid desiccant cooling system has been developed to determine the optimal system configuration and operating strategy. As for the Hot-dry, Hot-humid, and Mixed-humid, which are representative climatic conditions of summer in the United States, were selected. The system model has been simulated at a period of the highest electrical demand in a year with each climate. The solid oxide fuel cell-hybrid desiccant cooling system model has been simulated to compare the system efficiency at 4 case. The simulation results show that the solid oxide fuel cell-hybrid desiccant cooling system without a burner has the highest total efficiency of 77.7 % and the highest total coefficient of performance of 1.528 under electric load following operation. This study can provide the basic insight to establish the parametric analysis and control strategy in integrating the solid oxide fuel cell system and the desiccant cooling system.

Brain Storm Optimization Algorithm with an Adaptive Parameter Control Strategy for Finding Multiple Optimal Solutions

Real-world optimization problems often have multiple optimal solutions and simultaneously finding these optimal solutions is beneficial yet challenging. Brain storm optimization (BSO) is a relatively new paradigm of swarm intelligence algorithm that has been shown to be effective in solving global optimization problems, but it has not been fully exploited for multimodal optimization problems. A simple control strategy for the step size parameter in BSO cannot meet the need of optima finding task in multimodal landscapes and can possibly be refined and optimized. In this paper, we propose an adaptive BSO (ABSO) algorithm that adaptively adjusts the step size parameter according to the quality of newly created solutions. Extensive experiments are conducted on a set of multimodal optimization problems to evaluate the performance of ABSO and the experimental results show that ABSO outperforms existing BSO algorithms and some recently developed algorithms. BSO has great potential in multimodal optimization and is expected to be useful for solving real-world optimization problems that have multiple optimal solutions.

Controllability, reachability, observability performance analysis of ultra‐high voltage converter cascaded systems based on switched linear system model

SummaryUltra‐high voltage converters are usually employed to transform low voltage to high voltage in microgrids to produce renewable energy. However, it is challenging to develop control techniques due to the complicated structure of ultra‐high voltage converters and the numerous power switches and circuit components, which leads to poor dynamic characteristics of the system and a marked increase in fault rates. In order to understand the control theoretical characteristics of the ultra‐high voltage converter and provide a theoretical basis for circuit parameter analysis and control strategy design, this paper takes the boost converter cascaded system as an example to establish an accurate switching linear system model, discusses the control theoretical characteristics of the ultra‐high voltage converter in detail, and uses the decision theorem to draw the conclusion that the system is structurally controllable, reachable, and observable, simplifying the difficulty of analysis. The simulation and experimental findings show that the conclusion was drawn correctly. The research findings will give the boost converter cascaded systems modulation approach and circuit parameter design a theoretical foundation.

HPDE: A dynamic Hierarchical Population based Differential Evolution with novel diversity metric

Differential Evolution (DE) is one of the most effective methods for single-objective numerical optimization, however, the former state-of-the-art DE variants ignore the huge differences among the individuals in the population. To take full advantage of different individuals in the population, in this paper, a dynamic Hierarchical Population based Differential Evolution (HPDE) with novel diversity metric is proposed, and the HPDE algorithm has the following innovations: Firstly, the individuals in the hierarchical population are dynamically divided into two layers, the layer of elites, and the layer of ordinary individuals. Moreover, the ordinary layer individuals are further divided into secondary elites and inferior individuals. The individuals in the two layers employed different mutation strategies and the corresponding parameter control schemes. Secondly, a novel hierarchical population based mutation strategy is firstly proposed in our HPDE algorithm and the individuals in the elite layer employ the novel mutation strategy aiming at scouting the landscape of the objective while the ordinary individuals employ an improved “DE/target-pbest/1/bin” strategy aiming at getting a better balance between exploration and exploitation of the solution space. Thirdly, a Novel Parameter Control technique, namely NPC technique, is employed in the adaptation of control parameters during the evolution. Fourthly, a new metric of population diversity is proposed and premature convergence can be avoided by maintaining better population diversity. To evaluate the performance of our HPDE algorithm, intensive experiments are conducted on 88 benchmark functions from the CEC2013, CEC2014 and CEC2017 test suites, and the results show the competitiveness of our HPDE with six recent state-of-the-art DE variants, e.g. it obtained 28 similar or better performance improvements out of the total 30 benchmarks on 30D optimization in comparison with the winner algorithm, the LSHADE algorithm, of the CEC2014 competition and it also obtained 19 similar or better performance improvements out of the total 30 benchmarks on 30D optimization in comparison with the winner DE variant, the jSO algorithm, of the CEC2017 competition. Furthermore, the novel HPDE algorithm is utilized to estimate the parameters of the photovoltaic model, and the experiment results support its superiority as well.

Echo State Neural Network Based on an Improved Gray Wolf Algorithm Predicts Porosity through Logging Data.

In oil exploration and development, many reservoir parameters are very essential for reservoir description, especially porosity. The porosity obtained by indoor experiments is reliable, but human and material resources will be greatly invested. Experts have introduced machine learning into the field of porosity prediction but with the shortcomings of traditional machine learning models, such as hyperparameter abuse and poor network structure. In this paper, a meta-heuristic algorithm (Gray Wolf Optimization algorithm) is introduced to optimize the ESN (echo state neural) network for logging porosity prediction. Tent mapping, a nonlinear control parameter strategy, and PSO (particle swarm optimization) thought are introduced to optimize the Gray Wolf Optimization algorithm to improve the global search accuracy and avoid local optimal solutions. The database is constructed by using logging data and porosity values measured in the laboratory. Five logging curves are used as model input parameters, and porosity is used as the model output parameter. At the same time, three other prediction models (BP neural network, least squares support vector machine, and linear regression) are introduced to compare with the optimized models. The research results show that the improved Gray Wolf Optimization algorithm has more advantages than the ordinary Gray Wolf Optimization algorithm in terms of super parameter adjustment. The IGWO-ESN neural network is better than all machine learning models mentioned in this paper (GWO-ESN, ESN, BP neural network, least squares support vector machine, and linear regression) in terms of porosity prediction accuracy.

Alginate: Microbial production, functionalization, and biomedical applications

Alginates are natural polysaccharides widely participating in food, pharmaceutical, and environmental applications due to their excellent gelling capacity. Their excellent biocompatibility and biodegradability further extend their application to biomedical fields. The low consistency in molecular weight and composition of algae-based alginates may limit their performance in advanced biomedical applications. It makes microbial alginate production more attractive due to its potential for customizing alginate molecules with stable characteristics. Production costs remain the primary factor limiting the commercialization of microbial alginates. However, carbon-rich wastes from sugar, dairy, and biodiesel industries may serve as potential substitutes for pure sugars for microbial alginate production to reduce substrate costs. Fermentation parameter control and genetic engineering strategies may further improve the production efficiency and customize the molecular composition of microbial alginates. To meet the specific needs of biomedical applications, alginates may need functionalization, such as functional group modifications and crosslinking treatments, to achieve enhanced mechanical properties and biochemical activities. The development of alginate-based composites incorporated with other polysaccharides, gelatin, and bioactive factors can integrate the advantages of each component to meet multiple requirements in wound healing, drug delivery, and tissue engineering applications. This review provided a comprehensive insight into the sustainable production of high-value microbial alginates. It also discussed recent advances in alginate modification strategies and alginate-based composites for representative biomedical applications.

Parameter Optimization and Control Strategy of Hybrid Electric Vehicle Transmission System based on Improved GA Algorithm

Most of the traditional hybrid electric vehicles (HEVs) choose to optimize the transmission ratio parameters, and the parameter changes of the whole vehicle and other components are only calculated as fixed values. It is difficult to give consideration to the optimization of the economy and power of hybrid vehicles. Therefore, the research proposes to build the transmission ratio, the required power of the vehicle’s working mode, and other models through the dynamic analysis. The parameters of the whole vehicle are optimized on the basis of parameter matching. At the same time, this paper chooses to adopt a hybrid optimization algorithm, combining particle swarm optimization (PSO) and genetic algorithm (GA). The weighted average method and constraint method are used to design the fitness function. The simulation experiment is carried out by Cruise software and MATLAB. Compare the iterative fitness of the PSO-GA algorithm with the traditional PSO and GA algorithm. It can be concluded that PSO-GA converges at the 12th iteration, with an average optimal fitness of 0.5239, which is higher than the traditional algorithm. At the same time, the parameter optimization of PSO-GA and the simulated annealing algorithm is compared. It is found that in the same task, the gasoline consumption after SA algorithm optimization is 0.561 L, while the fuel consumption under PSO-GA algorithm optimization is 0.475 L. The method proposed in this study has improved the power and economy of the HEV model and is effective.

Sensitivity analysis of current distribution to critical operating parameters for polymer electrolyte membrane fuel cells

Suitable operating parameters can not only improve the uniformity of current distribution, but also extend the lifetime of fuel cells. Existing studies of critical operating parameters on the uniformity of local current distribution tend to be qualitative and lack quantitative indicators that affect the sensitivity. In addition, there is almost no physical quantity that can characterize the uniformity of current distribution, though the uniformity of current distribution is an important factor that affects the fuel cell lifetime. In this paper, a multi-domain physical model of a proton exchange membrane fuel cell based on segmentation and resistive grid is developed. A physical quantity index that can reflect the uniformity of current distribution is further proposed. In addition, the effects of five critical parameters on the uniformity of current distribution are investigated. Besides, the specific sensitivity values of each parameter to the proposed index are also quantitatively calculated. The research content in this paper can make up for the current vacancy research on the impacts of operating parameters on the uniformity of fuel cell current distribution. These results provide a reference for the performance optimization and critical operating parameter control strategies for proton exchange membrane fuel cells.

Research and implementation of frequency control strategy of islanded microgrids rich in grid-connected small hydropower

Due to the weak network architecture of mountainous power grid rich in grid-connected small hydropower, once the small hydropower units operate independently as a network when the upper power grid fails and loses power, the reliability of the power supply could be effectively improved. To cope with the problem of insufficient frequency regulation capacity of small hydropower units and the large risk of separation fault, this paper proposes a piecewise variable coefficient PID control strategy which improves the speed regulation control strategy of hydropower units, and a joint frequency regulation strategy of hydropower energy storage is designed to optimize the system operation state. Based on the MATLAB simulation platform, a hydropower energy storage microgrid model of mountainous power grid was built to verify the effectiveness of the segmented variable parameter control strategy. Using the proposed control strategy, the hydropower intelligent control terminal was developed and put into application in a certain area of Guangdong. Through simulation analysis and engineering applications, the practicality of the application of device engineering is verified to have good application and promotion value for a multi-hydropower mountainous power grid.

Two-Stage Genetic Algorithm for Scheduling Stochastic Unrelated Parallel Machines in a Just-in-Time Manufacturing Context

This paper considers a stochastic parallel machine scheduling problem in a just-in-time manufacturing context, in which its processing time can be described by a gamma or log-normal distribution. In order to obtain a high-performance schedule in a reasonable time, this work proposes a two-stage genetic algorithm with optimal computing budget allocation (OCBA) and improved Monte-Carlo Policy Evaluation (MCPE). In it, a genetic algorithm is selected as a main optimizer. An OCBA-based approach is developed to improve search efficiency, which is designed for two scenarios in a just-in-time manufacturing context. Different from most prior OCBA studies, this work considers that the stochastic processing time of jobs does not obey normal distribution. It extends the application area of OCBA by laying a theoretical foundation. A parameter control scheme based on MCPE is proposed, which aims to balance the global and local search in GA. To further enhance the efficiency and effectiveness of the proposed method, a two-stage framework is constructed. In the first stage, the performance is estimated roughly aiming at locating satisfactory solution regions. In the second stage, OCBA is incorporated to provide the reliable evaluation of excellent individuals. The theoretic interpretation of the proposed OCBA, and the convergence analysis results of the proposed method are presented. Various simulation results with benchmark and randomly generated cases validate that the proposed algorithm is more efficient and effective than several existing optimization algorithms. Note to Practitioners—A parallel machine scheduling problem under stochastic processing time is usually solved via meta-heuristic algorithms. However, their computational efficiency requires substantial improvement, especially for a stochastic optimization case that requires Monte Carlo sampling to estimate the actual objective function values in a precise manner. Most of them are parameter-sensitive, and choosing their proper parameters is highly challenging. For the first thorny issue, we develop an OCBA-based approach for determining the optimal numbers of simulations according to both prior knowledge and simulation results. In order to select proper control parameters of the proposed algorithm iteratively, we introduce a parameter control scheme based on MCPE. The combination of a meta-heuristic algorithm, OCBA and MCPE makes it possible to find high-quality solutions for the concerned scheduling problems in a short time. Theoretic analysis and numerical simulation results suggest that the proposed framework is valid and efficient. Hence, it can be readily applicable to practical systems, e.g., semiconductor manufacturing.