Parameter Optimization Method Research Articles

Efficient optimization parameter calibration method-based DEM simulation for compacted loess slope under dry–wet cycling

Dry–wet cycles can cause significant deterioration of compacted loess and thus affect the safety of fill slopes. The discrete element method (DEM) can take into account the non-homogeneous, discontinuous, and anisotropic nature of the geotechnical medium, which is more capable of reflecting the mechanism and process of instability in slope stability analysis. Therefore, this paper proposes to use the DEM to analyze the stability of compacted loess slopes under dry–wet cycles. Firstly, to solve the complex calibration problem between macro and mesoscopic parameters in DEM models, an efficient parameter optimization method was proposed by introducing the chaotic particle swarm optimization with sigmoid-based acceleration coefficients algorithm (CPSOS). Secondly, during the parameter calibration, a new indicator, the bonding ratio (BR), was proposed to characterize the development of pores and cracks in compacted loess during dry–wet cycles, to reflect the impact of dry–wet action on the degradation of bonding between loess aggregates. Finally, according to the results of parameter calibration, the stability analysis model of compacted loess slope under dry–wet cycling was established. The results show that the proposed optimization calibration method can accurately reflect the trend of the stress–strain curve and strength of the actual test results under dry–wet cycles, and the BR also reflects the degradation effect of dry–wet cycles on compacted loess. The slope stability analysis shows that the DEM reflects the negative effect of dry–wet cycles on the safety factor of compacted loess slopes, as well as the trend of gradual stabilization with dry–wet cycles. The comparison with the finite element analysis results verified the accuracy of the discrete element slope stability analysis.

Improved multi-input parameter optimization method for camera colorimetric characterization

In order to improve the accuracy of camera colorimetric characterization, a multi-input parameter optimization method was proposed in this paper. The input parameters of the traditional camera characterization method were generally RGB values; in the proposed method, the luminance parameter L was introduced in addition to RGB values, and the four-input parameters of RGBL were used as input parameters for the conversion model. In the experiment, 549 colors were uniformly selected from the Munsell Book of Color (Matte Edition), and the RGBL values and corresponding CIEXYZ values of the selected colors were measured by a spectroradiometer and three cameras, including an imaging luminance meter, respectively. Then, a polynomial model and a backpropagation (BP) neural network model were employed to establish the improved color conversion model with RGBL four-input parameters, which was compared with three-input parameter models to verify the effectiveness of the proposed method. Experimental results show that the proposed method can significantly improve the conversion accuracy and reduce the color difference with a maximum reduction of 57.7% in CIELAB.

A fast multi-objective optimization method for control parameters of high-speed maglev vehicle-bridge system

A fast multi-objective optimization method for control parameters of high-speed maglev vehicle-bridge system

Parameter optimization of the simulated moving bed system based on the IMOSCSO algorithm

AbstractThe optimization of operating parameters for the simulated moving bed (SMB) is complex. A parameter optimization method for the SMB system was proposed based on the improved multi‐objective sand cat swarm optimization (IMOSCSO) algorithm. The multi‐objective sand cat swarm optimization (MOSCSO) algorithm integrated the update and selection mechanism of the repository in the multi‐objective algorithm. Three strategies were proposed to improve the traditional MOSCSO algorithm for increased population diversity, global search capability, and convergence speed. First, the cubic chaotic map was used to initialize the population, which improved the uniformity of the population distribution. Second, including a variable spiral search strategy in the prey search phase enabled the sand cat swarm to explore more search paths to adjust its position. Third, the convergence speed was enhanced by incorporating the alert mechanism of the sparrow search algorithm. The improved algorithm was tested with standard test functions. The IMOSCSO algorithm outperformed other algorithms in terms of convergence and accuracy. Finally, the IMOSCSO algorithm optimized the system parameters of the SMB, demonstrating its practical applications.

Parameter Optimization Method for Metal Surface pBRDF Model Based on Improved Strawberry Algorithm

To study the polarization reflection characteristics of metal surfaces, a parameter optimization method for the polarization bidirectional reflection distribution function (pBRDF) model of metal surfaces based on the improved strawberry algorithm has been proposed. Firstly, the light scattering characteristics of metal surfaces were analyzed and a multi-parameter pBRDF model was constructed. Then, the working mechanism of the strawberry optimization algorithm was investigated and improved by introducing the chaotic mapping and Levy flight strategy to overcome the shortcomings, such as low convergence rate and easily falling into local optimum. Finally, the method proposed in this paper was validated by simulating open-source data from references and the obtained ones with a self-built experimental platform. The results show that the proposed method outperforms those by nonlinear least squares, particle swarm optimization and the original strawberry algorithm in fitting the detected degree of polarization (DOP) data, indicating the modeling accuracy was significantly improved and better suited to characterize the polarized reflection properties of metal surfaces.

On the Parameter Calibration Method for Dual-Spectral Triocular Camera

The dual-spectrum triocular camera system composed of a binocular visible light camera and mid-infrared camera is used for high-precision thermal fault detection and thermal field reconstruction of industrial equipment. The realization of its function depends on the high-precision camera parameter calibration. The difficulty lies in how to realize the infrared camera calibration quickly and improve the parameter accuracy of multi-lens camera. In this paper, according to the characteristics of the dual-spectral triocular camera system, the theoretical model is constructed, and the circular asymmetric calibration plate under infrared supplementary light is selected as the calibration object through experiments. A method for combining infrared image adaptive histogram enhancement and binarization processing based on the Sauvola algorithm is proposed to effectively calibrate the infrared camera. A global parameter optimization method based on traditional passive vision binocular stereo calibration is proposed. The optimization of experimental parameters finds the reprojection error value is 0.16, which meets the demand of high-precision calibration and solves the problem of difficult-to-calibrate weak image texture of infrared camera and the calibration accuracy of the camera under different imaging capabilities.

Application of the Taguchi method and RSM for process parameter optimization in AWSJ machining of CFRP composite-based orthopedic implants

Abstract Abrasive water suspension jet (AWSJ) machining on carbon fiber-reinforced polymer (CFRP) composite-based orthopedic implants yielded insightful results based on experimental data and subsequent statistical validations. Underwater AWSJ cutting consistently outperformed free air cutting, with numerical findings demonstrating its superiority. For instance, at #100 abrasive size and 5 mm standoff distance (SOD), the material removal rate (MRR) peaked at 2.44 g/min with a kerf width of 0.89 mm and a surface roughness (SR) of 9.25 µm. Notably, the increase in abrasive size correlated with higher MRR values, such as achieving 2.15 g/min at #120 grit and 3 mm SOD. Furthermore, optimization techniques like the Taguchi method and response surface methodology (RSM) were applied to refine machining parameters. These methodologies enhanced MRR, exemplified by achieving 2.10 g/min with #120 abrasive size and 5 mm SOD in underwater cutting conditions. The research explored the impact of key process parameters, namely, the speed, feed, and SOD on the MRR, kerf width, and SR in both free air cutting and underwater cutting conditions, which is one of the novel research endeavors in the domain of abrasive jet machining of composites.

Coil Parameter Optimization Method for Wireless Power Transfer System Based on Crowding Distance Division and Adaptive Genetic Operators

In a Magnetically Coupled Resonant Wireless Power Transfer (MCR-WPT) system, the magnetic coupling coil is one of the key factors that determines the system’s output power, transmission efficiency, anti-offset capability, and so on. This article proposes a coil parameter optimization method for a wireless power transfer system based on crowding distance division and adaptive genetic operators. Through optimizing the design of decision variables, such as the numbers of transmitting and receiving coil turns, the spacings between transmitting and receiving coil turns, the inner radii of the transmitting and receiving coils, and the vertical distance of the coil, the best transmission performance can be achieved. This study improves the NSGA-II algorithm through proposing a genetic operator algorithm for average crowding and high crowding populations based on adaptive operators, as well as a genetic operator algorithm for low crowding populations based on information entropy. These improved algorithms avoid problems inherent to traditional genetic operators such as fixed genetic proportions, do not easily cause the algorithm to fall into a local optimal solution, and show better convergence in the ZDT1–ZDT3 test functions. The optimization design method in this article is not only independent of commercial software such as ANSYS Maxwell 2021 R1, but can also significantly improve the calculation speed compared with traditional simulation software.

Design parameter optimization method for a prestressed steel structure driven by multi-factor coupling

Design parameter optimization method for a prestressed steel structure driven by multi-factor coupling

Development of a new environmentally friendly and efficient centrifugal variable diameter metering device.

The design of the maize metering device involves centrifugal variable diameter pneumatic and cleaning mechanisms, aiming to enhance the performance and power efficiency of pneumatic maize metering devices. Leveraging the impact of changes in centrifugal diameter and the guidance and positioning of airflow, we optimize the hole insert, seeding plate, seed limit board, and integrated front shell. This optimization facilitates the adjustment of both the quantity and posture of seed filling. As a result, seeds can form a uniform flow within the annular cavity, reducing the wind pressure necessary for regular operation and decreasing power consumption. A quadratic regression orthogonal rotation combination experiment is conducted using a self-made experiment bench, considering ground speed, wind pressure, and seeding rate as the experiment factors. Furthermore, a comparative experiment involving a novel centrifugal variable-diameter type metering device. The results indicate optimal seeding performance when the ground speed is 13.2 km/h, the wind pressure is 1.2 kPa, and the feeding rate is 25 seeds/s. Under these conditions, the quality of feed index reaches 95.20%, the multi-index is 3.87%, and the miss index is 0.93%. Findings reveal that the developed seed metering device achieved a quality of feed index exceeding 93.00% across varying speeds of 12~18 km/h, aligning with the production requirements. Moreover, the actual power consumption of Type B and C is about 85.00% and 98.00% lower than Type A, standing at only 32.90 W at 18 km/h. The COP of Type C is about 86 times and 12 times that of Type A and B, respectively, meeting the demands for efficient production of maize seed metering devices. In comparison to traditional design and structural parameter optimization methods for maize seed metering device, this study is helpful to the sustainable development of maize industry and reduce environmental pollution.

Learning Distributed Parameters of Land Surface Hydrologic Models Using a Generative Adversarial Network

AbstractLand surface hydrologic models adeptly capture crucial terrestrial processes with a high level of spatial detail. Typically, these models incorporate numerous uncertain, spatially varying parameters, the specification of which can profoundly impact the simulation capabilities. There is a longstanding tradition wherein parameter calibration has served as the conventional procedure to enhance model performance. However, calibrating distributed land surface hydrologic models presents a great challenge, often resulting in uneven spatial performance due to the compression of information inherent in model outputs and observations into a single‐value objective function. To address this problem, we propose a novel Generative Adversarial Network‐based Parameter Optimization (GAN‐PO) method. By leveraging a deep neural network to discern model spatial biases, we train a generative network to produce spatially coherent parameter fields, minimizing distinctions between simulations and observations. By leveraging neural network‐based surrogate models to make the physical model differentiable, we employ GAN‐PO to calibrate the Variable Infiltration Capacity (VIC) model against evapotranspiration (ET) over China's Huaihe basin. The results show that GAN‐PO can diminish errors in simulated ET derived from default parameters across nearly all grid cells within the study region, surpassing the conventional calibration approach based on the parameter regionalization technique. Ablation analysis indicates that relying solely on the traditional loss could lead to deteriorated model performance, underscoring the crucial role of the discriminator. Notably, due to the discriminator's explicit identification of model spatial biases, GAN‐PO excels in maintaining spatial consistency, outperforming the state‐of‐the‐art differentiable parameter learning (dPL) method in terms of model spatial performance.

Broad learning system based on maximum multi-kernel correntropy criterion

The broad learning system (BLS) is an effective machine learning model that exhibits excellent feature extraction ability and fast training speed. However, the traditional BLS is derived from the minimum mean square error (MMSE) criterion, which is highly sensitive to non-Gaussian noise. In order to enhance the robustness of BLS, this paper reconstructs the objective function of BLS based on the maximum multi-kernel correntropy criterion (MMKCC), and obtains a new robust variant of BLS (MKC-BLS). For the multitude of parameters involved in MMKCC, an effective parameter optimization method is presented. The fixed-point iteration method is employed to further optimize the model, and a reliable convergence proof is provided. In comparison to the existing robust variants of BLS, MKC-BLS exhibits superior performance in the non-Gaussian noise environment, particularly in the multi-modal noise environment. Experiments on multiple public datasets and real application validate the efficacy of the proposed method.

Operation Parameters Optimization Method of Coal Flow Transportation Equipment Based on Convolutional Neural Network

Operation Parameters Optimization Method of Coal Flow Transportation Equipment Based on Convolutional Neural Network

Broad learning system based on maximum multi-kernel correntropy criterion

The broad learning system (BLS) is an effective machine learning model that exhibits excellent feature extraction ability and fast training speed. However, the traditional BLS is derived from the minimum mean square error (MMSE) criterion, which is highly sensitive to non-Gaussian noise. In order to enhance the robustness of BLS, this paper reconstructs the objective function of BLS based on the maximum multi-kernel correntropy criterion (MMKCC), and obtains a new robust variant of BLS (MKC-BLS). For the multitude of parameters involved in MMKCC, an effective parameter optimization method is presented. The fixed-point iteration method is employed to further optimize the model, and a reliable convergence proof is provided. In comparison to the existing robust variants of BLS, MKC-BLS exhibits superior performance in the non-Gaussian noise environment, particularly in the multi-modal noise environment. Experiments on multiple public datasets and real application validate the efficacy of the proposed method.

The Specific Application and Meaning Expression of Folk Art in Urban Landscape Design Under the Optimized Interactive Genetic Algorithm

In the major cities with many high-rise buildings in contemporary China, land resources are becoming more and more scarce, and the urban ecological environment is in urgent need of recycling, and due to the blind imitation of Western culture and design mode and the neglect of China traditional regional culture, the urban landscape lacks interaction, resonance, and sense of belonging with citizens, and the phenomenon of landscape similarity emerges in various cities, focusing on the landscape space of urban complexes. There are also these problems. Urban residents urgently need a third space that can adjust their physical, mental, and spiritual needs. How to design an urban complex landscape that meets the aesthetic needs and humanistic needs of contemporary cities and has regional characteristics has become the first important task of my research. Folk art is an artistic treasure created by the working people in their production and life. Folk art is the embodiment of cultural regionality and the foundation of national culture. It awakens people’s awareness of the importance of local culture, awakens people’s sense of belonging, and is closer to the local public life. Today, the living soil and social and humanistic environment of folk art are in the process of urbanization in China, and there is a trend of gradual disappearance of lifestyle changes. How to make the contemporary urban complex landscape an organic soil for the survival, expression, application, and development of folk art is an important task in contemporary urban landscape design. Based on optimization, related concepts such as symbols, folk art symbols, urban complexes, urban complex landscape design, etc. have been sorted out. The relevant experimental results show that the construction land accuracy of the logistic regression model based on genetic algorithm has increased from 78.0% to 85.3%. kappa increased from 74.5% to 81.2%. Research shows that the logistic regression parameter optimization method based on genetic algorithm has better simulation effect than the conventional logistic regression method and is more suitable for the situation of unbalanced data distribution and many data features in the simulation of large-scale urban land dynamic changes.

Wave spectral partitioning for the SWIM spectrometer based on the wave age and parameter optimization method

The paper proposes a wave spectral partitioning method for the surface waves investigation and monitoring (SWIM) spectrometer based on the wave age and parameter optimization (WA-PO) method. The wave age criterion is a way to identify waves as wind waves or swells, and parameter optimization methods enable the modeling of wave spectra. The combination of the two can use the spectrum models to fit the SWIM spectra based on clarifying whether the spectrum models belong to wind waves or swells, which achieves spectral partitioning. The WA-PO method consists of four steps: peaks region dividing, spectral peaks searching, objective function constructing, and model parameters optimizing. The results show that compared with the partition products provided by the SWIM, the partition parameters obtained from the WA-PO method are closer to those of the Integrated Ocean Waves for Geophysical and other Applications (IOWAGA) numerical hindcast dataset developed from the WAVEWATCH III (WW3). The WA-PO method can prevent over-partitioning of the wave spectra and detect wind-wave components hidden in the swell partition.

OPTIMIZATION OF CYBER-PHYSICAL SYSTEM PARAMETERS BASED ON INTELLIGENT IOT SENSORS DATA

The optimization of cyber-physical systems (CPS) parameters is researched with focusing on the integration of computation, networking, and physical processes with the Internet of Things (IoT). The use of intelligent IoT sensors is crucial for collecting real-time data, which is necessary for enhancing the efficiency, reliability, and performance of CPS. Various methods of CPS parameters optimization are analyzed and categorized into model-based approaches, data-driven approaches, and hybrid approaches. The model-based approaches rely on mathematical models to describe CPS behavior and use optimization algorithms like linear programming and evolutionary algorithms to predict system responses and optimize parameters. But, the limitations of model-based approaches are related to complex systems with uncertain or dynamic behavior. The data-driven approaches are more suitable for complex cyber-physical systems. These approaches utilize machine learning and data analytics techniques to extract patterns from sensor data, which are then used to adjust system parameters. The hybrid approaches combine elements of both model-based and data-driven methods. The method of cyber-physical system parameters optimization based on intelligent IoT sensors data processing is developed with using of distributed neural network. The optimization problem is formulated with constraints for the system parameters. The neural network mathematical model and learning algorithm are proposed. The performed research shows the importance of developing optimization methods for CPS parameters based on intelligent IoT sensor data, considering the evolving nature of IoT technology. The integrating intelligent sensors into CPS offers new opportunities for optimizing system performance but also presents challenges in data management and security that should be addressed in future.

Comparison of parameter optimization methods for a runoff forecast model based on a support vector machine

Long-term flow prediction is crucial in water resource management, with support vector regression (SVR) commonly used for runoff prediction. This paper aims to clarify SVR parameter optimization through a case study, focusing on the penalty coefficient (C), the kernel function parameter (γ), and the insensitive loss coefficient (ε), using different heuristic methods including particle swarm optimization (PSO), a complex method (CM), and simulated annealing (SA). A runoff prediction model based on SVR was established to forecast the monthly inflow runoff of Zipingpu Reservoir. The study evaluates the applicability of each optimization method for the SVM-based monthly streamflow forecast model. Results indicate CM has the best operational efficiency with an average running time of less than 2 s, significantly lower than PSO and SA. SA shows the best stability with an average standard deviation of fitness values of 0.00032, significantly lower than PSO's 0.0015 and CM's 0.021. The SA-SVM model reduces RMSE and MARE by 8.35% and 15% respectively, compared to the standard SVM model, while NS and R improve by 11.67% and 6.57%, achieving the highest prediction accuracy. Thus, the SA algorithm is the most applicable, followed by the PSO algorithm.

Multi-Objective Process Parameter Optimization of Ultrasonic Rolling Combining Machine Learning and Non-Dominated Sorting Genetic Algorithm-II.

Ultrasonic rolling is an effective technique for enhancing surface integrity, and surface integrity is closely related to fatigue performance. The process parameters of ultrasonic rolling critically affect the improvement of surface integrity. This study proposes an optimization method for process parameters by combining machine learning (ML) with the NSGA-II. Five ML models were trained to establish relationships between process parameters and surface residual stress, hardness, and surface roughness by incorporating feature augmentation and physical information. The best-performing model was selected and integrated with NSGA-II for multi-objective optimization. Ultrasonic rolling tests based on a uniform design were performed, and a dataset was established. The objective was to maximize surface residual stress and hardness while minimizing surface roughness. For test specimens with an initial surface roughness of 0.54 µm, the optimized process parameters were a static pressure of 900 N, a spindle speed of 75 rpm, a feed rate of 0.19 mm/r, and rolling once. Using optimized parameters, the surface residual stress reached -920.60 MPa, surface hardness achieved 958.23 HV, surface roughness reduced to 0.32 µm, and contact fatigue life extended to 3.02 × 107 cycles, representing a 52.5% improvement compared to untreated specimens and an even more significant improvement over without parameter optimization.

Optimal performance and preliminary parameter matching for hydrogen fuel cell powertrain system of electric aircraft

Fuel cells are true net-zero carbon emission power sources for aircraft, which is highly sensitive to weight. In the initial phase of adapting hydrogen fuel cell systems for aircraft powertrains, preliminary design parameter matching remains premature. An explicit method for the performance optimization of aircraft hydrogen fuel cell powertrain systems and a process of preliminary parameter matching are proposed to address this problem. Performance and weight models of the fuel cell stack and its auxiliaries, the cathode air compressor subsystem, and the cooling subsystem are designed, and system performance at various altitudes and power output levels is calculated. The aircraft flight mission performance is synthesized and considered in the optimization process. The optimization result of system performance and the corresponding design parameters are then graphically illustrated as tern plots. Unlike the traditional iterative preliminary system parameter matching and optimization method, which explores the design space non-directionally and converges to a single local optimal point, the proposed explicit method sweeps the design space globally and obtains a group of design points with acceptable optimality. The system design process is boosted by a compact iterative loop. In the optimization practice, the cruise powertrain specific energy is improved by 6.5%. The relationship between specific system design parameters and system performance is displayed globally by the resulting tern plots. Multiple design guidelines are observed and proposed, and design scenarios are directly obtained from the graphs for further engineering processes.