Global Optimal Parameters Research Articles

A Comparative Study of Parameter Identification Fusion Methods With a High-Precision Supercapacitor Model for Electric Vehicles

Abstract To ensure energy storage system operates reliably for electric vehicles, it is vital to accurately identify supercapacitor model parameters in applications. In recent years, most of the algorithms focus on lithium-ion batteries, but few are reported to be used for supercapacitors. To fill this research gap, many algorithms and corresponding fusion methods for supercapacitors are designed in this study. First, seven popular intelligent optimization algorithms are selected to identify the supercapacitor model parameters, and the identification results are discussed in detail. Then, considering a single algorithm cannot guarantee convergence to all global optimal model parameters over state-of-charge (SOC) intervals, five fusion methods for supercapacitor parameter identification have been developed by combining information fusion technology. Finally, voltage errors are statistically analyzed to validate the effectiveness of the five proposed fusion methods. The results show that the five fusion methods can further enhance the global prediction performance of the supercapacitor model, particularly the reverse search-based parameter identification fusion (PIF-RS) method, which has better accuracy and reliability with respect to the maximum (Max) error, mean error, and root mean square (RMS) error decreasing by at least 10.1191%, 17.0024%, and 17.0989%, respectively.

Joint ISAR imaging and azimuth scaling under low SNR using parameterized compensation and calibration method with entropy minimum criterion

In general, the method of conventional motion compensation for inverse synthetic aperture radar (ISAR) imaging is divided into translational motion compensation (TMC) and rotational motion compensation (RMC) in sequence. TMC is the premise of rotational compensation and the most critical procedure is range alignment. However, the deviation of echo correlation results in the poor performance of range alignment under low signal-to-noise ratio (SNR). Therefore, a new high-resolution ISAR imaging and azimuth scaling method under low SNR using parameterized compensation and calibration is proposed in this paper. Firstly, the target motion is modeled, in which translational motion is modeled as formula of the polynomial coefficient vector. In addition, entropy minimization corresponding to echo signal with compensation term based on coefficients is taken as objective function. Moreover, the particle swarm optimization (PSO) algorithm is utilized to search the global optimal parameters to be estimated precisely and efficiently to implement joint motion compensation and azimuth scaling. The experimental results from both simulated and real data verify the effectiveness and robustness of the method.

High-Performance Adaptive Weak Fault Diagnosis Based on the Global Parameter Optimization Model of a Cascaded Stochastic Resonance System.

Stochastic resonance (SR), as a type of noise-assisted signal processing method, has been widely applied in weak signal detection and mechanical weak fault diagnosis. In order to further improve the weak signal detection performance of SR-based approaches and realize high-performance weak fault diagnosis, a global parameter optimization (GPO) model of a cascaded SR system is proposed in this work. The cascaded SR systems, which involve multiple multi-parameter-adjusting SR systems with both bistable and tri-stable potential functions, are first introduced. The fixed-parameter optimization (FPO) model and the GPO models of the cascaded systems to achieve optimal SR outputs are proposed based on the particle swarm optimization (PSO) algorithm. Simulated results show that the GPO model is capable of achieving a better SR output compared to the FPO model with rather good robustness and stability in detecting low signal-to-noise ratio (SNR) weak signals, and the tri-stable cascaded SR system has a better weak signal detection performance compared to the bistable cascaded SR system. Furthermore, the weak fault diagnosis approach based on the GPO model of the tri-stable cascaded system is proposed, and two rolling bearing weak fault diagnosis experiments are performed, thus verifying the effectiveness of the proposed approach in high-performance adaptive weak fault diagnosis.

Micro-damage analysis and numerical simulation of composite solid propellant based on in situ tensile test

Abstract In order to quantitatively analyze the mesoscopic damage process of hydroxyl-terminated polybutadiene composite solid propellant under external load, periodic boundary conditions were applied to the representative volume element model based on sample composition and morphology, the mixed matrix containing aluminum powder was homogenized, and the hyperelastic matrix damage and bilinear/exponential particle–matrix interface cohesive model with initial damage were compiled through the secondary development of Abaqus. At the same time, a data interaction platform was constructed by means of Python and MATLAB, matrix and cohesion parameters were inverted according to the optimization algorithm and experimental data, and the whole process of propellant damage and fracture was simulated from the mesoscopic perspective. The results show that combining the adaptive particle swarm optimization algorithm and the Hooke–Jeeves algorithm can achieve the global optimal parameter inversion in 102 calculations, compared with the single local search algorithm, which can cut about 11% of the objective function values. Considering the matrix damage and the exponential cohesion model with initial damage, the optimal objective function value is 0.01635, which can more accurately simulate the propellant damage and fracture process compared with 0.02136 of a bilinear cohesion model.

Indirect prediction of remaining useful life for lithium-ion batteries based on improved multiple kernel extreme learning machine

Lithium-ion batteries (LIBs) will undergo aging after a certain period of operation. Predicting RUL in advance is critical to ensure a reliable energy supply from the battery system. To address the problem of poor robustness of the single-core extreme learning machine model in predicting the RUL of LIBs, a multiple kernel extreme learning machine (MKELM) integrating radial basis function (RBF) and Poly kernel function is proposed. Firstly, introduce the sparrow search algorithm improved by logistic chaotic mapping (ISSA) to optimize the parameter selection of the MKELM, which overcomes the inefficiency of parameter selection, and improves the efficiency of the global optimal parameter selection. Secondly, from the charging/discharging processes, extract the health indicators (HIs) and examine the correlation capacity via methods of Pearson, Spearman, and Kendall. Finally, validate the ISSA-MKELM with the NASA battery dataset, and the prediction results are compared with traditional methods. Results show that, with the lowest prediction error (less than 2.38 %), ISSA-MKELM has good state tracking fit and excellent computational efficiency performance but also has strong generalization and robustness.

Optimal design of phase‐locked loop with frequency‐adaptive prefilter based on the accurate small‐signal model

AbstractFrequency‐adaptive prefilters are widely used in the phase‐locked loop (PLL) to suppress input disturbances. However, the parameters of the PLL and prefilters are often designed separately or based on an inaccurate model. The positive feedback effect introduced by the frequency adaption (FA) is usually ignored, leading to non‐optimal performance. Based on the accurate small‐signal model with a particular focus on the FA, this paper proposes an optimal design method for these advanced PLLs. The PLL based on dual second‐order generalized integrator (DSOGI) is taken as an example to demonstrate the principle. With different gain margins, the optimal damping factors of DSOGI and PLL, as well as the natural frequency of PLL, are obtained using the proposed method. Then the global optimal parameters are obtained by evaluating the settling time and overshoot. The analysis shows that the truly optimal parameters are quite different with those provided in the literature, and the dynamic performance is significantly enhanced without degrading the steady‐state performance. Experimental results are presented to verify the theoretical analysis.

A Novel End-to-End Unsupervised Change Detection Method with Self-Adaptive Superpixel Segmentation for SAR Images

Change detection (CD) methods using synthetic aperture radar (SAR) data have received significant attention in the field of remote sensing Earth observation, which mainly involves knowledge-driven and data-driven approaches. Knowledge-driven CD methods are based on the physical theoretical models with strong interpretability, but they lack the robust features of being deeply mined. In contrast, data-driven CD methods can extract deep features, but require abundant training samples, which are difficult to obtain for SAR data. To address these limitations, an end-to-end unsupervised CD network based on self-adaptive superpixel segmentation is proposed. Firstly, reliable training samples were selected using an unsupervised pre-task. Then, the superpixel generation and Siamese CD network were integrated into the unified framework to train them end-to-end until the global optimal parameters were obtained. Moreover, the backpropagation of the joint loss function promoted the adaptive adjustment of the superpixel. Finally, the binary change map was obtained. Several public SAR CD datasets were used to verify the effectiveness of the proposed method. The transfer learning experiment was implemented to further explore the ability to detect the changes and generalization performance of our network. The experimental results demonstrate that our proposed method achieved the most competitive results, outperforming seven other advanced deep-learning-based CD methods. Specifically, our method achieved the highest accuracy in OA, F1-score, and Kappa, and also showed superiority in suppressing speckle noise, refining change boundaries, and improving detection accuracy in a small area change.

Vibroacoustic Transfer Characteristics of Underwater Cylindrical Shells Containing Complex Internal Elastic Coupled Systems

Cylindrical shells containing complex elastic coupling systems are the main structural form of underwater vehicles. Therefore, in this paper, the vibroacoustic radiation problem of underwater cylindrical shells containing complex internal elastic coupling systems is studied. Firstly, the dynamics model of the complex elastic coupled system is established through the method of integrated conductivity. The sound pressure distribution law and the general magnitude relationship between the performance index of hydroacoustic radiation and vibration isolation are investigated through numerical simulation. A strategy of global sensitivity analysis and related parameter optimization is carried out, by applying the Sobol’ method to the dynamics model. It could be concluded that the main flap of sound pressure at low and medium frequencies appears in the direction of the excitation force or the perpendicular to the excitation force, the magnitudes correspondence between the vibration level drop—power flow—hydroacoustic radiation at low frequencies can be expressed as a relatively simple function, and the vibroacoustic transmission of the system at lower order resonance frequencies is dominated by the parameter configuration of the vibration isolation device, while at higher frequencies is more influenced by the modalities of the base structure. The transfer power flow and the level drop are used as objective functions to optimise the acoustic radiation index of the coupled system, with the best results obtained when the transfer power flow and the level drop are used together as objective functions.

PSOSVRPos: WiFi indoor positioning using SVR optimized by PSO

Wireless fidelity (WiFi) indoor positioning has attracted the attention of thousands of researchers. It faces many challenges, and the primary problem is the low positioning accuracy, which hinders its widespread applications.To improve the accuracy, we propose a WiFi indoor positioning algorithm based on support vector regression (SVR) optimized by particle swarm optimization (PSO), termed PSOSVRPos. SVR algorithm devotes itself to solving localization as a regression problem by building the mapping between signal features and spatial coordinates in high dimensional space. PSO algorithm concentrates on the global-optimal parameter estimation of the SVR model. The positioning experiment is conducted on an open dataset (1511 samples, 154 features). The PSOSVRPos algorithm could achieve positioning accuracy with a mean absolute error of 1.040 m, a root mean square error (RMSE) of 0.863 m and errors within 1 m of 59.8%.Experimental results indicate that the PSOSVRPos algorithm is a precise approach for WiFi indoor positioning as it reduces the RMSE (35%) and errors within 1 m (14%) compared with state-of-the-art algorithms such as convolutional neural network (CNN) based methods.

Quantifying factory-scale CO2/CH4 emission based on mobile measurements and EMISSION-PARTITION model: cases in China

Development of the measurement-based carbon accounting means is of great importance to supplement the traditional inventory compilation. Mobile CO2/CH4 measurement provides a flexible way to inspect plant-scale CO2/CH4 emissions without the need to notify factories. In 2021, our team used a vehicle-based monitor system to conduct field campaigns in two cities and one industrial park in China, totaling 1143 km. Furthermore, we designed a model based on sample concentrations to evaluate CO2/CH4 emissions, EMISSION-PARTITION, which can be used to determine global optimal emission intensity and related dispersion parameters via intelligent algorithm (particle swarm optimization) and interior point penalty function. We evaluated the performance of EMISSION-PARTITION in chemical, coal washing, and waste incineration plants. The correlations between measured samples and rebuilt simulated ones were larger than 0.76, and RMSE was less than 11.7 mg m−3, even with much fewer samples (25). This study demonstrated the wide applications of a vehicle-based monitoring system in detecting greenhouse gas emission sources.

Predictive Current Control of Sensorless Linear Permanent Magnet Synchronous Motor

In the vector control system of a tubular oscillating permanent magnet synchronous linear motor, it is difficult to obtain accurate feedback information from the conventional mechanical sensors under bad and complex working conditions. This paper presents a new predictive current control designed to estimate the speed of the tubular oscillation permanent magnet synchronous linear motor. It implements two control techniques: The first technique is using the sliding-mode observer’s speed observer for speed estimation. The second is to design a deadbeat predictive current control to replace the PI regulator in the conventional current loop; it solves the difficulties of global optimization and PI parameter setting. The simulation and experimental results show that this method gives a good dynamic performance.

Using physical property surrogate models to perform accelerated multi-fidelity optimization of force field parameters.

Accurate representations of van der Waals dispersion-repulsion interactions play an important role in high-quality molecular dynamics simulations. Training the force field parameters used in the Lennard Jones (LJ) potential typically used to represent these interactions is challenging, generally requiring adjustment based on simulations of macroscopic physical properties. The large computational expense of these simulations, especially when many parameters must be trained simultaneously, limits the size of training data set and number of optimization steps that can be taken, often requiring modelers to perform optimizations within a local parameter region. To allow for more global LJ parameter optimization against large training sets, we introduce a multi-fidelity optimization technique which uses Gaussian process surrogate modeling to build inexpensive models of physical properties as a function of LJ parameters. This approach allows for fast evaluation of approximate objective functions, greatly accelerating searches over parameter space and enabling the use of optimization algorithms capable of searching more globally. In this study, we use an iterative framework which performs global optimization with differential evolution at the surrogate level, followed by validation at the simulation level and surrogate refinement. Using this technique on two previously studied training sets, containing up to 195 physical property targets, we refit a subset of the LJ parameters for the OpenFF 1.0.0 (Parsley) force field. We demonstrate that this multi-fidelity technique can find improved parameter sets compared to a purely simulation-based optimization by searching more broadly and escaping local minima. Additionally, this technique often finds significantly different parameter minima that have comparably accurate performance. In most cases, these parameter sets are transferable to other similar molecules in a test set. Our multi-fidelity technique provides a platform for rapid, more global optimization of molecular models against physical properties, as well as a number of opportunities for further refinement of the technique.

Kernel Path for ν-Support Vector Classification.

It is well known that the performance of a kernel method is highly dependent on the choice of kernel parameter. However, existing kernel path algorithms are limited to plain support vector machines (SVMs), which has one equality constraint. It is still an open question to provide a kernel path algorithm to ν -support vector classification ( ν -SVC) with more than one equality constraint. Compared with plain SVM, ν -SVC has the advantage of using a regularization parameter ν for controlling the number of support vectors and margin errors. To address this problem, in this article, we propose a kernel path algorithm (KP ν SVC) to trace the solutions of ν -SVC exactly with respect to the kernel parameter. Specifically, we first provide an equivalent formulation of ν -SVC with two equality constraints, which can avoid possible conflicts during tracing the solutions of ν -SVC. Based on this equivalent formulation of ν -SVC, we propose the KP ν SVC algorithm to trace the solutions with respect to the kernel parameter. However, KP ν SVC traces nonlinear solutions of kernel method rather than the errors of loss function, and it is still a challenge to provide the algorithm that guarantees to find the global optimal model. To address this challenging problem, we extend the classical error path algorithm to the nonlinear kernel solution paths and propose a new kernel error path (KEP) algorithm that ensures to find the global optimal kernel parameter by minimizing the cross validation error. We also provide the finite convergence analysis and computational complexity analysis to KP ν SVC and KEP. Extensive experimental results on a variety of benchmark datasets not only verify the effectiveness of KP ν SVC but also show the advantage of applying KEP to select the optimal kernel parameter.

GADMA2: more efficient and flexible demographic inference from genetic data.

Inference of complex demographic histories is a source of information about events that happened in the past of studied populations. Existing methods for demographic inference typically require input from the researcher in the form of a parameterized model. With an increased variety of methods and tools, each with its own interface, the model specification becomes tedious and error-prone. Moreover, optimization algorithms used to find model parameters sometimes turn out to be inefficient, for instance, by being not properly tuned or highly dependent on a user-provided initialization. The open-source software GADMA addresses these problems, providing automatic demographic inference. It proposes a common interface for several likelihood engines and provides global parameters optimization based on a genetic algorithm. Here, we introduce the new GADMA2 software and provide a detailed description of the added and expanded features. It has a renovated core code base, new likelihood engines, an updated optimization algorithm, and a flexible setup for automatic model construction. We provide a full overview of GADMA2 enhancements, compare the performance of supported likelihood engines on simulated data, and demonstrate an example of GADMA2 usage on 2 empirical datasets. We demonstrate the better performance of a genetic algorithm in GADMA2 by comparing it to the initial version and other existing optimization approaches. Our experiments on simulated data indicate that GADMA2's likelihood engines are able to provide accurate estimations of demographic parameters even for misspecified models. We improve model parameters for 2 empirical datasets of inbred species.

Global optimization of process parameters for low-temperature SiNx based on orthogonal experiments

Global optimization of process parameters for low-temperature SiNx based on orthogonal experiments

Quantitative Comparison on Permanent-Magnet Vernier Machines With Improved Flux Modulation Effect for Automatic Guided Vehicles

In this paper, the interior permanent-magnet vernier (PMV) machines and the conventional PMV machine are quantitatively compared regarding the requirements for electric automatic guided vehicles (AGV). The key feature of the interior PMV machines is to place the middle iron pole sandwiched by PMs in the slots between the salient rotor poles. As a result, three types of interior PMV machines with different PM arrangements, namely two V-type PMV (V-PMV) and one U-type PMV (U-PMV) machines, are developed. Due to the improved flux modulation effect, the V-PMV and U-PMV machines can respectively provide over 20% and 30% higher torque density than the conventional PMV counterpart, under a high power factor level of 0.86. The working principle is illustrated based on the field modulation theory. A comparative study among the conventional PMV and interior PMV machines is conducted in terms of electromagnetic performances and dynamic responses, after performing global optimization of design parameters. Finally, a prototype is manufactured and tested for experimental validations.

Periodic energy transmission and regulation of photovoltaic-phase change material-thermoelectric coupled system under space conditions

Phase change material (PCM) as highly efficient temperature control material has been used in photovoltaic-thermoelectric (PV-TE) coupling system to achieve higher power generation efficiency. The periodic energy balance and PCM regeneration are the key factors that affect the temperature control effect and system total performance. In present paper, a numerical model for space PV-PCM-TE system was established with the composite of paraffin and aluminum foam as PCM. Firstly, the impact of periodic energy imbalance on PCM regeneration characteristics and system overall performance was investigated under space conditions. Then, to regulate the periodic energy transmission of the coupling system, a global structural parameters optimization was performed, and the optimal parameter combination was derived and validated, where porosity of aluminum foam, thickness of PCM layer, thermal concentration factor, length of TE legs, and surface area ratio are 0.94, 4.08 mm, 25.83, 1.09 mm and 1.80, respectively. The validation results show that after optimization, the periodic energy balance and PCM full utilization and regeneration can be achieved. The average total efficiency of optimal case is 30.72%, which is a little lower than that with the surface area ratio of 7.0 (31.77%), but the average power density is 49.64% higher. The present work validates that periodic energy balance design is important for space PV-PCM-TE system and the global parameter optimization method is an effective optimization method, which is significant for the design and optimization of PCM based temperature control system.

Research on lung nodule recognition algorithm based on deep feature fusion and MKL-SVM-IPSO

Lung CAD system can provide auxiliary third-party opinions for doctors, improve the accuracy of lung nodule recognition. The selection and fusion of nodule features and the advancement of recognition algorithms are crucial improving lung CAD systems. Based on the HDL model, this paper mainly focuses on the three key algorithms of feature extraction, feature fusion and nodule recognition of lung CAD system. First, CBAM is embedded into VGG16 and VGG19, and feature extraction models AE-VGG16 and AE-VGG19 are constructed, so that the network can pay more attention to the key feature information in nodule description. Then, feature dimensionality reduction based on PCA and feature fusion based on CCA are sequentially performed on the extracted depth features to obtain low-dimensional fusion features. Finally, the fusion features are input into the proposed MKL-SVM-IPSO model based on the improved Particle Swarm Optimization algorithm to speed up the training speed, get the global optimal parameter group. The public dataset LUNA16 was selected for the experiment. The results show that the accuracy of lung nodule recognition of the proposed lung CAD system can reach 99.56%, and the sensitivity and F1-score can reach 99.3% and 0.9965, respectively, which can reduce the possibility of false detection and missed detection of nodules.

Application Research of Partial Differential Equation Optimization Image Analysis in National Traditional Physical Education Teaching

In this paper, the initial parameters of C-V level set image recognition are optimized by using the global optimization characteristics of cultural algorithm, and a cultural algorithm C-V level set image recognition model is proposed, which is abbreviated as the CC-V model. The initial population space of the cultural algorithm is used to set the initial recognition parameters in a large range, and the population evolution is continuously optimized and guided by the situation knowledge and normative knowledge of the reliability space, so as to realize the global optimization of the recognition parameters and to timely terminate by judging the change of the image entropy fitness value. Through the analysis and comparison of the experimental results, the CC-V model has a better recognition effect than the C–V model. The partial differential equation image recognition model is applied to the video image sequence for moving target recognition. The background model is constructed by the block statistical histogram. The background difference method is used to locate the video moving target, and the minimum circumscribed rectangle of the multitarget positioning is used as the initial outline of the model recognition. The research results show that the nonintellectual factors of each group of ethnic traditional sports have an obvious effect on the application value of people, and the application effect of nonintellectual factors produced by different groups will also vary. All ethnic traditional sports groups show higher application value to nonintellectual character factors. The comprehensive application value of nonintelligence factors of the combat confrontation item group, accuracy item group, and endurance item group is relatively higher than the comprehensive application value of nonintelligence factors of other item groups.

Design and optimization of a novel compliant planar parallelogram mechanism utilizing initially curved beams

High-precision displacement platforms are of great significance in micro/nanoscale manipulation, ultraprecision manufacturing, biological manipulation, and scanning probe microscopes. This paper presents a novel compliant planar parallelogram mechanism (CPPM) for high-precision translational motion utilizing 8 flexible initially curved beams (ICBs). Compared with the existing high-precision displacement platforms for traditional motion, CPPM combines the characteristics of large motion ranges, bidirectional anti-buckling and high load-bearing. Moreover, this new mechanism introduces more design parameters compared to traditional parallelogram mechanisms, thus generating other desired characteristics by optimizing these parameters. In this paper, a general mathematical model is first derived for quick design and model-based optimization, followed by nonlinear finite element method (FEM) validation. Three desired performance characteristics in compliant translational mechanisms are proposed thereafter to obtain three optimized designs through global optimization of the preset geometric parameters. Experimental results verify the accuracy of the mathematical model and demonstrate centimeter-level high-precision positioning within a large motion range. Finally, a comparative discussion of the anti-buckling mechanisms for translation is presented.