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Differential evolution is capable of searching for the optimum for different types of optimization problems with a few inputs, which has gained worldwide popularity. In this paper, we propose a parameters adaptation scheme based on the stagnation ratio (PASR), which regards the stagnation ratio (STR) as the indicator for adjusting the control parameters and greediness parameter. To be specific, when the STR is larger than the predefined threshold, exploration is advocated. In this circumstance, larger control parameters and a greediness parameter are adopted. However, when the STR is smaller than the predefined threshold, exploitation is preferred. In this case, smaller control parameters and a greediness parameter are utilized. Further, when the stagnation lasts for a long period, a generation-based selection (GBS) scheme is developed to help it escape from the local optimum and stagnation. Comparative experiments have been implemented on the CEC2017 to testify the effectiveness of adaptive differential evolution with the stagnation termination mechanism (STMDE) and its components. The competitiveness of the STMDE is also verified via comparing it to top-performing DE variants in the practical optimization problem selected from the CEC2011. [ABSTRACT FROM AUTHOR]

This study delves into the effectiveness of two time integration techniques, namely the adaptive implicit–explicit (imp–exp) and explicit–explicit (exp–exp) methods, which stand as efficient formulations for tackling intricate systems characterized by multiple time scales. The imp–exp technique combines implicit and explicit procedures by employing implicit formulations for faster components and explicit calculations for slower ones, achieving high accuracy and computational efficiency. Conversely, the exp–exp method, a variation of explicit methods with sub-cycling, excels in handling locally stiff systems by employing smaller sub-steps to resolve rapid changes while maintaining stability. For both these approaches, numerical damping may be activated by adaptive time integration parameters, allowing numerical dissipation to be locally applied, if necessary, as a function of the considered discrete model and its computed responses, enabling a highly effective numerical dissipative algorithm. Furthermore, both these techniques stand as very simple and straightforward formulations as they rely solely on single-step displacement–velocity relations, describing truly self-starting procedures, and they stand as entirely automated methodologies, requiring no effort nor expertise from the user. This work provides comparative studies of the adaptive imp–exp and exp–exp approaches to assess their accuracy and efficiency across a wide range of scenarios, with emphasis on geophysical applications characterized by multiscale problems, aiming to establish under which circumstances one approach should be preferred over the other. [ABSTRACT FROM AUTHOR]

Abstract Differential Evolution (DE) stands as a potent global optimization algorithm, renowned for its application in addressing a myriad of practical engineering issues. The efficacy of DE is profoundly influenced by its control parameters and mutation strategies. In light of this, we introduce a refined DE algorithm characterized by adaptive parameters and dual mutation strategies (APDSDE). APDSDE inaugurates an adaptive switching mechanism that alternates between two innovative mutation strategies: DE/current-to-pBest-w/1 and DE/current-to-Amean-w/1. Furthermore, a novel parameter adaptation technique rooted in cosine similarity is established, with the derivation of explicit calculation formulas for both the scaling factor weight and crossover rate weight. In pursuit of optimizing convergence speed whilst preserving population diversity, a sophisticated nonlinear population size reduction method is proposed. The robustness of each algorithm is rigorously evaluated against the CEC2017 benchmark functions, with empirical evidence underscoring the superior performance of APDSDE in comparison to a host of advanced DE variants.

This study delves into the effectiveness of two time integration techniques, namely the adaptive implicit–explicit (imp–exp) and explicit–explicit (exp–exp) methods, which stand as efficient formulations for tackling intricate systems characterized by multiple time scales. The imp–exp technique combines implicit and explicit procedures by employing implicit formulations for faster components and explicit calculations for slower ones, achieving high accuracy and computational efficiency. Conversely, the exp–exp method, a variation of explicit methods with sub-cycling, excels in handling locally stiff systems by employing smaller sub-steps to resolve rapid changes while maintaining stability. For both these approaches, numerical damping may be activated by adaptive time integration parameters, allowing numerical dissipation to be locally applied, if necessary, as a function of the considered discrete model and its computed responses, enabling a highly effective numerical dissipative algorithm. Furthermore, both these techniques stand as very simple and straightforward formulations as they rely solely on single-step displacement–velocity relations, describing truly self-starting procedures, and they stand as entirely automated methodologies, requiring no effort nor expertise from the user. This work provides comparative studies of the adaptive imp–exp and exp–exp approaches to assess their accuracy and efficiency across a wide range of scenarios, with emphasis on geophysical applications characterized by multiscale problems, aiming to establish under which circumstances one approach should be preferred over the other.

Differential Evolution (DE) stands as a potent global optimization algorithm, renowned for its application in addressing a myriad of practical engineering issues. The efficacy of DE is profoundly influenced by its control parameters and mutation strategies. In light of this, we introduce a refined DE algorithm characterized by adaptive parameters and dual mutation strategies (APDSDE). APDSDE inaugurates an adaptive switching mechanism that alternates between two innovative mutation strategies: DE/current-to-pBest-w/1 and DE/current-to-Amean-w/1. Furthermore, a novel parameter adaptation technique rooted in cosine similarity is established, with the derivation of explicit calculation formulas for both the scaling factor weight and crossover rate weight. In pursuit of optimizing convergence speed whilst preserving population diversity, a sophisticated nonlinear population size reduction method is proposed. The robustness of each algorithm is rigorously evaluated against the CEC2017 benchmark functions, with empirical evidence underscoring the superior performance of APDSDE in comparison to a host of advanced DE variants. [ABSTRACT FROM AUTHOR]

Aiming at the problems of the classical DBSCAN algorithm that it is difficult to determine the global optimal parameters and misjudge the outliers, the algorithm firstly started from the perspective of selecting the optimal parameters, generated the Eps and MinPts lists by the distribution characteristics of the data set, and then carried out the full combination operation of the parameters in the two lists, and then performed the clustering of the different combinations of the parameters in order, so as to search for the parameter corresponding to the point of the highest accuracy rate. Finally, from the perspective of outlier, the three-branch decision-making idea was combined with the outlier detection LOF algorithm. This algorithm was compared and analysed with a variety of clustering algorithms, and the results show that this algorithm can achieve the fully automated selection of the global optimal parameters as well as improve the accuracy of the clustering algorithm. [ABSTRACT FROM AUTHOR]

The arbitrary selection of the Crow Search Algorithm (CSA) parameters, the Awareness Probability (AP) and the Flight Length (fl) results in poor convergence performance and efficiency even if the CSA performs well when solving global optimization problems. In fact, a more search process variety is the outcome of increasing the fl. Furthermore, a higher value of the fl is preferred to guide the optimization process in the direction of global search, whilst a lower fl value directs the algorithm in the direction of local search. In this regard, this study presents a unique Fuzzy Logic adaptive CSA (FL-CSA) for a freestanding Photovoltaic System (PVS) that is based on a Fuzzy Logic (FL) supervisor. Therefore, it is recommended to use the FL supervisor for the online AP and fl, tuning to get superior performance in terms of quick convergence to the GMPP and in terms of high efficiency. Three distinct situations are used to validate the efficacy and speed of the proposed FL-CSA through numerical modeling and experimental testing. The results demonstrate the superiority of the suggested FL-CSA over other traditional approaches, including the Conventional CSA (CCSA), the Conventional Particle Swarm Optimization (CPSO), and the Perturb and Observe (P&O) method. It is true that the maximum power generated by the PVS is extracted by the suggested FL-CSA-based MPPT with average efficiency of 99.93%, whereas the CCSA, the CPSO and P&O record average efficiency of 99.78%, 99.50% and 96.40%, respectively. Additionally, the proposed FL-CSA-based MPPT strategy reduces the convergence time by an average of 42%, 63% and 61%, respectively, in comparison to the CCSA, the CPSO and the P&O MPPT methods.

Differential evolution is capable of searching for the optimum for different types of optimization problems with a few inputs, which has gained worldwide popularity. In this paper, we propose a parameters adaptation scheme based on the stagnation ratio (PASR), which regards the stagnation ratio (STR) as the indicator for adjusting the control parameters and greediness parameter. To be specific, when the STR is larger than the predefined threshold, exploration is advocated. In this circumstance, larger control parameters and a greediness parameter are adopted. However, when the STR is smaller than the predefined threshold, exploitation is preferred. In this case, smaller control parameters and a greediness parameter are utilized. Further, when the stagnation lasts for a long period, a generation-based selection (GBS) scheme is developed to help it escape from the local optimum and stagnation. Comparative experiments have been implemented on the CEC2017 to testify the effectiveness of adaptive differential evolution with the stagnation termination mechanism (STMDE) and its components. The competitiveness of the STMDE is also verified via comparing it to top-performing DE variants in the practical optimization problem selected from the CEC2011.

To enhance the solution quality and accelerate the convergence speed, teaching quality evaluation-based differential evolution algorithm with Levy flight and adaptive parameters is proposed in this paper, which contains two modifications. One is the new multi-population strategy inspired by teaching quality evaluation. In this multi-population strategy, first, the entire evolving population is partitioned into several subpopulations of equal size and each subpopulation is evaluated and assigned an evaluation score like the way in teaching quality evaluation, according to which new individuals are generated. Then the subpopulations integrate and are re-divided to make some individuals in the better subpopulations spread to other subpopulations, thus guiding the evolution of the entire population and accelerating the convergence speed. The other modification is the improved mutation strategy combined with Levy flight and adaptive parameters. Levy flight can avoid the algorithm stuck with the local optimum, and adaptive parameters can change dynamically during the evolutionary process, hence reducing the adverse effects caused by parameter sensitivity. To validate the effectiveness of the proposed algorithm, extensive test experiments on benchmark functions are carried out, and the two modifications are studied separately at length. At the end, the proposed algorithm is applied to synthesize linear sparse array antennas. The results show that it can reduce peak sidelobe level further and enhance the performance of linear sparse arrays. [ABSTRACT FROM AUTHOR]

Automatic identification system (AIS) data record a ship's position, speed over ground (SOG), course over ground (COG), and other behavioral attributes at specific time intervals during a ship's voyage. At present, there are few studies in the literature on ship trajectory classification, especially the clustering of trajectory segments, to measure the multi-dimensional information of trajectories. Therefore, it is necessary to fully utilize the multi-dimensional information from AIS data when utilizing ship trajectory classification methods. Here, we propose a ship trajectory classification method based on multi-attribute trajectory similarity metrics which utilizes the following steps: (1) Improve the Douglas–Peucker (DP) algorithm by considering the SOG and COG; (2) use a multi-attribute symmetric segmentation path distance (MSSPD) for the similarity metric between trajectories; (3) cluster the segmented sub-trajectories based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm; (4) adaptively determinate the optimal input parameters based on the proposed comprehensive clustering performance metrics. The proposed method was tested on real AIS data from Bohai Sea waters, and the experimental results show that the algorithm can accurately cluster the ship trajectory groups and extract traffic distributions in key waters. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

UAVs have been widely used in deformation monitoring because of their high availability and flexibility. However, the quality of UAV images is affected by changing attitude and surveying environments, resulting in a low monitoring accuracy. Cross-shaped markers are used to improve the accuracy of UAV monitoring due to their distinct center contrast and absence of eccentricity. However, existing methods cannot rapidly and precisely detect these markers in UAV images. To address these problems, this paper proposes an adaptive Radon-transform-based marker detection and localization method for UAV displacement measurements, focusing on two critical detection parameters, namely, the radius of marker information acquisition and the edge width of the cross-shaped scoring template. The experimental results show that the marker detection rate is 97.2% under different combinations of flight altitudes, radius ratios of marker information acquisition, and marker sizes. Furthermore, the root mean square error of detection and localization is 0.57 pixels, significantly surpassing the performance and accuracy of other methods. We also derive the critical detection radius and appropriate parameter combinations for different heights to further improve the practicality of the method.

For semantic segmentation tasks, there are problems in using multi-branch structures to enrich feature maps and aggregate different branches of feature maps in a certain network depth, such as the insufficient richness of feature maps and the incomplete aggregation of feature maps. Given multi-branch feature maps and branch aggregation, this study proposes a lightweight method, called multi-branch aggregation atrous spatial pyramidal pooling, by introducing an attention mechanism and CARAFE to enrich the features of the feature maps, giving the feature maps adaptive parameters, periodically adjusting the adaptive parameters, and aggregating the feature maps in both the vertical and horizontal directions. First, the atrous pyramid is retained and the attention mechanism and CARAFE are used to handle the pooling features to obtain 10 different feature maps. Secondly, giving each feature map a cascaded adaptive parameter and periodically adjusting the adaptive parameter to promote or suppress certain feature maps prevents the model from being at a local minimum or saddle point for long periods due to aleatory uncertainty. Finally, the feature maps are vertically aggregated, horizontally aggregated, and weighted for summation. This work demonstrates a competitive performance on the benchmark dataset, with an improvement of 1.88% in MPA, 0.66% in FWIoU, and 1.29% in MIoU compared to atrous spatial pyramid pooling. The benchmark datasets include PASCAL VOC 2012 and CIFAR-100. [ABSTRACT FROM AUTHOR]

The convergence speed and the diversity of the population plays a critical role in the performance of particle swarm optimization (PSO). In order to balance the trade-off between exploration and exploitation, a novel particle swarm optimization based on the hybrid learning model (PSO-HLM) is proposed. In the early iteration stage, PSO-HLM updates the velocity of the particle based on the hybrid learning model, which can improve the convergence speed. At the end of the iteration, PSO-HLM employs a multi-pools fusion strategy to mutate the newly generated particles, which can expand the population diversity, thus avoid PSO-HLM falling into a local optima. In order to understand the strengths and weaknesses of PSO-HLM, several experiments are carried out on 30 benchmark functions. Experimental results show that the performance of PSO-HLM is better than other the-state-of-the-art algorithms.

The technological development and exploration of the proton exchange membrane fuel cell have relied on several mathematical models. However, despite the wide variety of models, the equivalent electronic circuit model is the most suitable for describing electrical behavior, designing electronic interfaces, and analyzing control and reliability strategies. In addition to the fact that this type of model is scarce to model the fuel cell voltage, an equivalent electronic circuit model that depends only on the input current has not been reported (in general, the reported mathematical models take into account additional variables such as humidity, temperature, pressure, etc.). For this reason, this work focuses on developing an equivalent electronic circuit model for the fuel cell voltage that depends only on the input current. Besides, the configuration of the proposed circuit (one voltage source, two capacitors, and three resistors) is simpler than the circuits proposed in previous works. To validate the model and its parameters, current tests from $1.2~kW$ Nexa® fuel cell power module were used. The comparison between the experimental data and the developed model confirms the efficiency of the equivalent electronic circuit model to reproduce the fuel cell voltage as a function of the current.

In order to determine a way to be able to shorten the smelting time of low-carbon ferrochrome alloys and to strengthen the temperature control capability of the AOD converter during the smelting process, this article establishes a mechanism model of the rate of oxygen supply and carbon content change in the smelting process as well as the temperature of the reaction fluid in the converter. The physical and chemical reactions of the smelting process and the actual smelting data are used as the basis. The temperature and carbon content in the smelting converter are considered as the output quantity and the rate of oxygen supply is considered as the input quantity. An expert internal model control framework is built. In addition, an adaptive parameter adjustment mechanism is added to the control framework, taking into account data such as the maximum gas supply rate and the maximum limiting temperature of the actual production process. This method improves the smelting speed and smelting accuracy compared to the general internal mode control method. Finally, according to this method, with the use of the built low-carbon ferrochrome alloy centralized control system for smelting, the system shortens the smelting time by 12.79% compared with the general method and controls the converter temperature below 1950 K, which achieves the expected goal according to the simulation and actual smelting results. [ABSTRACT FROM AUTHOR]

In this paper, a chord consonance model is established via complex law analysis to improve the accuracy of music emotion calculation. To address the coupling and the uncertainty of the complex law, quantum adaptive method is applied to the chord consonance calculation. Musical tones, with subjective difference and uncertainty, are constructed into interval consonance quantum state using parameter adaptive method. Quantum logic gates are introduced to describe the coupling between musical tones to establish the chord consonance model. Then, a mixed state density matrix is designed to optimize the calculation of consonance. Experimental results are provided to verify the validity and accuracy of the modelling. [ABSTRACT FROM AUTHOR]

Traditional strategy for multi-fault repair of distribution relies on decision-maker to make the subjective judgment,which is apparently lack of scientific basis. It is easy to make mistakes in judgment. The emergency repair resources cannot be reasonably allocated and the power supply cannot be restored at the first time. In order to solve the problem,an optimization model for multi-fault repair in distribution system is established,which has comprehensively considered multi-group collaboration and rush-repair order. The non dominated sorting genetic algorithm (NSGA)-II algorithm with adaptive parameters is designed. After the Pareto front is obtained,the knee solution algorithm based on angle selection can directly solve a relatively ideal scheme without the participation of decision maker. Simulation results of the distribution network in a town show that the adaptive parameter adjustment strategy can improve the global search ability in the early stage of population evolution and the local search ability in the later stage of evolution. The final repairing scheme obtained by the knee solution algorithm based on angle selection can be selected directly from multiple feasible schemes,which can reduce the burden of decision-makers and it is suitable for practical emergency repair work.

• A new method for evaluating roundness error based on improved bat algorithm was proposed. • Added chaos inertia weight and optimized adaptive pulse loudness and pulse emission rate. • Gaussian low-pass filter was used to preprocess the data. • Experimental results validated the stability, accuracy and timeliness of the improved bat algorithm. Roundness error is one of the core indicators for evaluating the geometric accuracy of round parts mechanical products, directly affecting product performance and service life. In the field of metrology, the accurate, rapid and standardized assessment of roundness error has always been a hot topic. This paper proposed a new method for evaluating roundness error based on improved bat algorithm. This method was based on the new geometric product specification for extraction, filtering and fitting. Based on the advantages of solving optimization problems using the bat algorithm, transformed the roundness error evaluation problem using the minimum zone method into a problem of using the bat algorithm to find the center of the minimum zone circle, then further solved for the roundness error value. Also, this algorithm effectively avoided falling into local optimal solutions by introducing chaotic inertia weights during the velocity update phase, it improved the accuracy and speed of the evaluation. Introduced adaptive parameters during the loudness and emission rate update phases to enhance the algorithm's global search capability, it enhanced the stability of the algorithm. The experimental results indicated, the efficiency of roundness error evaluation in the new method was significantly better than that of the genetic algorithm, the simplex algorithm and the emperor penguin algorithm. There was a significant improvement in evaluation accuracy and stability. The feasibility of this method in roundness error evaluation using the minimal zone method was validated. [ABSTRACT FROM AUTHOR]

Automatic identification system (AIS) data record a ship’s position, speed over ground (SOG), course over ground (COG), and other behavioral attributes at specific time intervals during a ship’s voyage. At present, there are few studies in the literature on ship trajectory classification, especially the clustering of trajectory segments, to measure the multi-dimensional information of trajectories. Therefore, it is necessary to fully utilize the multi-dimensional information from AIS data when utilizing ship trajectory classification methods. Here, we propose a ship trajectory classification method based on multi-attribute trajectory similarity metrics which utilizes the following steps: (1) Improve the Douglas–Peucker (DP) algorithm by considering the SOG and COG; (2) use a multi-attribute symmetric segmentation path distance (MSSPD) for the similarity metric between trajectories; (3) cluster the segmented sub-trajectories based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm; (4) adaptively determinate the optimal input parameters based on the proposed comprehensive clustering performance metrics. The proposed method was tested on real AIS data from Bohai Sea waters, and the experimental results show that the algorithm can accurately cluster the ship trajectory groups and extract traffic distributions in key waters.

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) are involuntary by-products of incomplete combustion and are highly toxic to humans and the environment. The Malaysian peat is often acidic or extremely acidic having high levels of chlorine and/or other organic acids that act as catalysts or precursors in PCDD/Fs formation. This study aims to predict PCDD/Fs emissions in peat soil using an artificial neural network (ANN) approach based on limited emission data and selected physico-chemical properties. The ANN's prediction performance is affected by uncertainties in its initial connection weights. To improve prediction performance, an optimisation algorithm, termed differential evolution (DE), is used to optimise the ANN's initial connection weights and bias. The study adopts several ANNs with fixed architecture to predict PCDD/Fs emissions, each consisting of a multilayer perceptron (MLP) with a backpropagation algorithm. Eight input variables and one output variable were adopted to train and test various neural network architectures using real-world datasets. The model optimisation procedure was conducted to ascertain the network architecture with the best predictive accuracy. The evolved ANN based on 5 hidden neurons, with the assistance of self-adaptive ensemble-based differential evolution with enhanced population sizing (SAEDE-EP), successfully produced the lowest MSE test (6.1790 × 10−3) and highest R2 (0.97447) based on the mean among the other HNs. An evolutionary-optimised ANN-based methodology is a viable solution to predict PCDD/Fs in peat soil. It is cost-effective for pollution control, environmental monitoring and capable of aiding authorities prevent PCDD/Fs exposure, e.g., during a fire. [Display omitted] • PCDD/Fs prediction model is built based on an evolutionary-optimised ANN. • Adopted limited datasets of PCDD/Fs emissions and physico-chemical of peat samples. • Differential evolutionary algorithm improved accuracy and error estimates. • Cost-effective solution for pollution and environmental monitoring. [ABSTRACT FROM AUTHOR]

In order to improve the management efficiency of peach orchards, this paper considers the cooperative operation scheme of multiple unmanned tractors. According to the actual situation, this paper constructs the path planning model of multiple unmanned tractors in a standard peach orchard, designs the objective function to optimize the total turning time and total operating time according to the tractor driving parameters, and solves it by improving the differential evolution algorithm. Aiming at the premature convergence problem, the permutation matrix is introduced to represent the driving paths of multiple unmanned tractors. Then, the dynamic parameters are adopted to make the parameters change with the number of iterations, and the elite selection strategy is used to eliminate the redundant feasible solutions. An Adaptive Elite Differential Evolution (AEDE) algorithm suitable for multi-tractor path optimization is proposed. The results show that, compared with the traditional Differential Evolution algorithm (Differential Evolution, DE), the total turning time and total operating time in the rectangular peach orchard optimized by AEDE are reduced by 3.34% and 0.87%, respectively. Compared with the block operation, the total turning time and total operating time of the AEDE-optimized rectangular peach orchard operation path were reduced by 37.37% and 9.47%, respectively. Experiments show that AEDE, which optimizes the operating path of multi-tractors in standard peach orchards, is able to improve the efficiency and reduce the operating time. [ABSTRACT FROM AUTHOR]

To suppress the influence of power fluctuation in the DC microgrid system, virtual DC motor (VDM) control is applied to the energy storage converter for improving the stability of the power system. Due to the fixed parameters adopted in the traditional VDM control strategy, the dynamic response of the system cannot be taken into account. Based on the traditional VDM control strategy, a small signal model is established to analyze the influence of the moment of inertia J and the damping coefficient D on the control performance. Moreover, the effects of J and D on the dynamic response of DC bus voltage under power fluctuation are thoroughly analyzed. Consequently, a novel parameter-adaptation-based VDM control strategy is proposed by realizing the adaptive adjustment of J and D. Furthermore, the comparative simulation of three kinds of operating conditions are carried out, and the adaptive parameters are determined through comparative simulation under three kinds of operating conditions. The results show that the proposed control strategy has great superiorities in control effect and flexibility, and the anti-interference and dynamic response abilities of the system are significantly improved, by comparing with the traditional control strategies.

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a multi-temporal image change detection algorithm based on adaptive parameter estimation, which is used to solve the problems of severe interference of coherent speckle noise and the retention of detailed information about changing regions in synthetic aperture radar remote sensing images. The change area in the initial differential image has local consistency and global prominence. By detecting the significant area to locate similar change areas, the coherent speckle noise outside the area can be eliminated. The use of hierarchical FCM clustering to automatically generate training samples can improve the reliability of training samples. In addition, in order to increase the distinction between the changed area and the non-changed area, a sparse automatic encoder is used to extract the changed features and generate a change detection map. Experiments using 4 sets of SAR images show that the algorithm can effectively reduce the effect of speckle noise on detection accuracy, the extraction of changing areas is more complete and meticulous, and the false detection rate is greatly reduced. Since the images in different time phases will be disturbed by weather, clouds, sea water, etc., the target segmentation algorithm can be used to extract the target of interest and highlight the changing area. Principal component analysis and k-means clustering method are used to reduce the influence of isolated pixels, and change information is extracted to obtain different images. The experiment uses four sets of image data of islands and reefs. The experiment proves that the algorithm can well eliminate external interference, improve the accuracy of change detection, and have a good detection effect on the area of islands and reefs. The adaptive parameter estimation plays a good role in the detection of changing areas, and the visual effect is better, which can improve the accuracy of the detection results.

For the problem of load frequency control (LFC) in multi-area interconnected power systems, a method of linear active disturbance rejection control (LADRC) that uses Deep Q-Network (DQN) to obtain controller parameters in real-time is designed. The main idea of combining the DQN and the LADRC is to equate the state and action in the environment of Reinforcement Learning (RL) to the output of the power system and the parameters of the controller, which will make the controller have learning ability and better adaptability. In order to achieve a better training effect, some improvements have been made to the action selection strategy and reward function. The proposed method is applied to the three-area and four-area interconnected power systems under the influence of disturbances, and the simulation results show the effectiveness of the proposed method. Compared with Proportional-Integral-Derivative (PID) controller and LADRC with fixed parameters, the method proposed in this paper has a better response effect in terms of overshoot and settling time of the power system. [ABSTRACT FROM AUTHOR]

Fully adaptive control charts are efficient statistical process control means to monitor a quality characteristic affecting the outcome of a manufacturing process. Usually, the performance of these adaptive charts is investigated in processes characterised by the possibility of the occurrence of a single assignable cause. However, this assumption is frequently far from reality, because a process shift to the out-of-control condition can be the consequence of several assignable causes, which can occur at the same time or independently. In this paper, we investigate the economic-statistical design of a variable-parameter (Vp) Shewhart control chart monitoring the process mean in the presence of multiple assignable causes. We develop a Markov chain that models the occurrence of several assignable causes leading to progressive process deterioration and calling for different corrective actions. A benchmark of examples has been generated to compare the performance of the Vp control chart with other adaptive control charts and the fixed-parameter control chart. The obtained results reveal the economic superiority of the Vp control chart. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University: Engineering Science is the property of Editorial Office of Journal of Zhengzhou University: Engineering Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal Of Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban is the property of Editorial Department of Journal of Sichuan University Natural Science Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, an optimized approach is proposed to enhance the performance of combined explicit–implicit time-domain analyses. In this context, an entirely automated explicit-implicit adaptive time-marching procedure is discussed as well as an optimization algorithm is introduced to compute the adopted time-step value of the analysis, so that the amount of explicit and implicit elements occurring along the model may be optimally provided, in terms of computational efficiency. The proposed formulation is very effective, allowing evaluating highly accurate responses considering much reduced computational efforts. At the end of the manuscript, numerical applications are presented, illustrating the excellent performance of the proposed formulation. [ABSTRACT FROM AUTHOR]

In order to improve the management efficiency of peach orchards, this paper considers the cooperative operation scheme of multiple unmanned tractors. According to the actual situation, this paper constructs the path planning model of multiple unmanned tractors in a standard peach orchard, designs the objective function to optimize the total turning time and total operating time according to the tractor driving parameters, and solves it by improving the differential evolution algorithm. Aiming at the premature convergence problem, the permutation matrix is introduced to represent the driving paths of multiple unmanned tractors. Then, the dynamic parameters are adopted to make the parameters change with the number of iterations, and the elite selection strategy is used to eliminate the redundant feasible solutions. An Adaptive Elite Differential Evolution (AEDE) algorithm suitable for multi-tractor path optimization is proposed. The results show that, compared with the traditional Differential Evolution algorithm (Differential Evolution, DE), the total turning time and total operating time in the rectangular peach orchard optimized by AEDE are reduced by 3.34% and 0.87%, respectively. Compared with the block operation, the total turning time and total operating time of the AEDE-optimized rectangular peach orchard operation path were reduced by 37.37% and 9.47%, respectively. Experiments show that AEDE, which optimizes the operating path of multi-tractors in standard peach orchards, is able to improve the efficiency and reduce the operating time.

This article aims to propose and experimentally validate a static-dynamic electrical model of a proton exchange membrane (PEM) electrolyzer. The originality of this work concerns the cell voltage modeling according to static and dynamic operations. Indeed, the cells of the PEM electrolyzer may be subjected to degradations due to the operating conditions and current ripple generated by power electronics. Hence, cell voltage response and efficiency may be affected. For this reason, it is crucial to model each cell voltage to investigate the degradation and wear effects mainly caused by the dynamic operating conditions met when coupling with renewable energy sources and current ripple from power electronics. To develop an accurate model, static and dynamic operations are investigated on a commercial-400 W PEM electrolyzer stack. To enhance the accuracy of the model in replicating the real behavior of the electrolyzer, the parameters of the model are adapted according to the input current. The comparison between the experimental data and the developed model has enabled confirming the effectiveness of the model to reproduce the cell voltage static and dynamic behavior according to the input current. • An adaptive cell voltage model has been developed for PEM electrolyzer. • The developed model is based on static and dynamic characterization. • Dynamics and current ripple issues have been discussed. • An equivalent electrical model has been used to model each cell voltage. • Experimental tests have been performed to validate the e_ectiveness of the model. [ABSTRACT FROM AUTHOR]

UAVs have been widely used in deformation monitoring because of their high availability and flexibility. However, the quality of UAV images is affected by changing attitude and surveying environments, resulting in a low monitoring accuracy. Cross-shaped markers are used to improve the accuracy of UAV monitoring due to their distinct center contrast and absence of eccentricity. However, existing methods cannot rapidly and precisely detect these markers in UAV images. To address these problems, this paper proposes an adaptive Radon-transform-based marker detection and localization method for UAV displacement measurements, focusing on two critical detection parameters, namely, the radius of marker information acquisition and the edge width of the cross-shaped scoring template. The experimental results show that the marker detection rate is 97.2% under different combinations of flight altitudes, radius ratios of marker information acquisition, and marker sizes. Furthermore, the root mean square error of detection and localization is 0.57 pixels, significantly surpassing the performance and accuracy of other methods. We also derive the critical detection radius and appropriate parameter combinations for different heights to further improve the practicality of the method.

In spite of being one of the most popular optimization methods, Nelder–Mead's simplex search algorithm with the default choice of parameters performs poorly on high-dimensional problems. The work presented here concerns such values of the Nelder–Mead algorithm's parameters that help improve the convergence and success rate of the algorithm in high dimensions. In this work, a novel way of assigning parameters to the Nelder–Mead simplex search algorithm is proposed. The proposed scheme is based on Chebyshev spacing points and adapts itself to the dimension of the problem. The numerical experiments conducted for this study show that the proposed scheme is better not just in comparison with the original Nelder–Mead algorithm but it outperforms the other existing adaptive schemes as well. [ABSTRACT FROM AUTHOR]

Compared to alkaline electrolyzers, PEM electrolyzers offer high current densities and can be coupled to renewable energy sources because of their fast responses to dynamics. The modeling of PEM electrolyzers is a challenging issue to reproduce its behavior and to design properly power electronics and its control without damaging a real electrolyzer. The input current may have an impact on the dynamics of the electrolyzer and must be taken into consideration to make a model more reliable. In this work, an equivalent electrical circuit to replicate accurately the dynamic behavior of the PEM electrolyzer subject to fast current change is investigated. Based on the input current, the parameters of the model can not be considered as constant. Hence, to improve the accuracy of the model, an adaptive static-dynamic electrical model is proposed and takes into consideration the change of input current. This model is validated by using a commercial-400 W PEM electrolyzer. The obtained results demonstrate the effectiveness of the model to predict the PEM stack voltage. • Development and validation of a static-dynamic model for PEM electrolyzer voltage. • Analysis of the voltage dynamic behavior in a PEM electrolyzer. • The dynamic behaviors have been modeled through an electronic circuit. • Parameter estimation for a static-dynamic mathematical model. • Simulation and comparison of a mathematical model and PEMelectrolyzer voltage. [ABSTRACT FROM AUTHOR]

Traditionally, hydro turbine governor applications mainly rely on classical proportional–integral–derivative controllers. A classical controller can perform optimally only at the operating point chosen during the controller design. Since hydro power plants are highly non-linear systems alternative control approaches based on adaptive parameters are needed. Historically, due to the limited computation capabilities of microprocessors and programmable logic controllers (PLCs) used in hydro turbine governors, adaptive control schemes were not frequently applied. However, the latest generation of microprocessors and PLCs facilitate the application of adaptive control scheme based on predictive control algorithm for plants with faster dynamic behaviour. In that regard, this study introduces an adaptive controller based on model predictive control (MPC) algorithm developed and applied to a non-linear simulation model of a laboratory hydro power plant. The applied MPC algorithm is based on a linear prediction model whose parameters are identified offline for different operating points across the plant's operating range. The adaptive control scheme updates the prediction model parameters depending on the current operating point. Furthermore, the predictive control algorithm applied in this study is set up as a quadratic programming (QP) optimisation problem that is solved online using a QP solver in a form of Hildreth's algorithm. [ABSTRACT FROM AUTHOR]

In order to improve the performance of whale optimization algorithm for solving complex function optimization problems, this paper proposed an improved whale optimization algorithm based on adaptive parameters and niche technology. Firstly, the algorithm introduced an adaptive probability threshold to coordinate the global exploration and local development abilities. Then, the algorithm used adaptive position weights to adjust the whale position update formula to improve the convergence speed and search precision. Finally, the algorithm used preselection niche technology to avoid premature convergence. The results on 12 typical benchmark functions show that the improved algorithm has faster convergence speed and higher search precision than other comparison algorithms. It proves that the improvement strategy can effectively improve the performance of the whale optimization algorithm for solving complex function optimization problems. [ABSTRACT FROM AUTHOR]

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We study unbiased (1 + 1) evolutionary algorithms on linear functions with an unknown number n of bits with non-zero weight. Static algorithms achieve an optimal runtime of O (n (ln ⁡ n) 2 + ε) , however, it remained unclear whether more dynamic parameter policies could yield better runtime guarantees. We consider two setups: one where the mutation rate follows a fixed schedule, and one where it may be adapted depending on the history of the run. For the first setup, we give a schedule that achieves a runtime of (1 ± o (1)) β n ln ⁡ n , where β ≈ 3.552 , which is an asymptotic improvement over the runtime of the static setup. Moreover, we show that no schedule admits a better runtime guarantee and that the optimal schedule is essentially unique. For the second setup, we show that the runtime can be further improved to (1 ± o (1)) e n ln ⁡ n , which matches the performance of algorithms that know n in advance. Finally, we study the related model of initial segment uncertainty with static position-dependent mutation rates, and derive asymptotically optimal lower bounds. This answers a question by Doerr, Doerr, and Kötzing. [ABSTRACT FROM AUTHOR]

In this work, an entirely automatized adaptive semi-explicit/explicit numerical approach is proposed to analyse nonlinear dynamic models. In this methodology, the time integrators are spatially and temporally locally evaluated, allowing different values to be assigned to each element of the spatial discretization and to each time step of the analysis. Thus, a very flexible procedure is enabled, permitting the technique to properly locally adapt to the properties of the model, as well as to the computed results. The novel algorithm is very accurate and extremely efficient, since it deals with reduced systems of equations and it requires no iterative procedures for solution. The main features of the novel methodology can be summarized as follows: (i) it is simple; (ii) it is locally defined; (iii) it enables stabilized analyses; (iv) it is an efficient non-iterative single-solution procedure; (v) it provides enhanced accuracy; (vi) it enables advanced controllable algorithmic dissipation in the higher modes; (vii) it considers a link between the temporal and the spatial discretizations; (viii) it stands as a single-solve framework based on reduced systems of equations; (ix) it provides an updating criterion for the tangent nonlinear matrices; (x) under special conditions, it may reproduce the Central Difference Method; (xi) it is entirely automated, requiring no expertise from the user. • An effective time-marching procedure is proposed for nonlinear dynamics. • The method is entirely automatized, requiring no expertise from the user. • It is locally defined, introducing modified elements to guarantee stability. • It provides enhanced accuracy and advanced controllable numerical dissipation. • It enables reduced solver efforts and it stands as a non-iterative single-solution procedure. [ABSTRACT FROM AUTHOR]

Summary: In this work, an explicit‐implicit time‐marching procedure with model/ solution‐adaptive time integration parameters is proposed for the analysis of hyperbolic models. The two time integrators of the methodology are locally evaluated, enabling their different spatial and temporal distributions. The first parameter defines the explicit/implicit subdomains of the model, and it is defined in a way that stability is always ensured, as well as period elongation errors are reduced; the second parameter controls the dissipative properties of the methodology, allowing spurious high‐frequency modes to be properly eliminated, rendering reduced amplitude decay errors. In addition, the proposed explicit‐implicit approach allows contracted systems of equations to be obtained, reducing the computational effort of the analysis. The main features of the novel methodology can be summarized as follows: (i) it is simple; (ii) it is locally defined; (iii) it has guaranteed stability; (iv) it is an efficient noniterative single‐step procedure; (v) it provides enhanced accuracy; (vi) it enables advanced controllable algorithmic dissipation in the higher modes; (vii) it considers a link between the temporal and the spatial discretization; (viii) it stands as a single‐solve framework based on reduced systems of equations; (ix) it is truly self‐starting; and (x) it is entirely automatic. [ABSTRACT FROM AUTHOR]