Accuracy Of Parameter Extraction Research Articles

A threshold activation-based simplified Lv's transform algorithm for transient multi-component linear frequency modulation signals analysis.

The high sampling rate in modern digital systems generates a large scale of data. To address the computational burden, this paper proposes a threshold activation-based simplified Lv's transform (SLVT) algorithm to analyze the transient multi-component linear frequency modulation signals. Only the signal arrival can trigger the signal analysis. This mechanism alleviates the computation pressure because of the sparsity of signals. The threshold activation mechanism enables transient signal detection and sampling rate adjustment, thereby enhancing the efficiency and effectiveness of the analytical process. The simplified Lv's transform (LVT) removes redundant computations on stretch weighting and the Discrete Fourier Transform (DFT) in Lv's transform. SLVT uses the efficient Bluestein chirp-z algorithm to implement the stretch keystone transform. The comparison results show that SLVT reduces the computational complexity of the original LVT by at least 30.8%. This algorithm exhibits superior performance compared to other advanced signal processing methods, such as discrete chirp Fourier transform, fractional Fourier transform, and Radon Wigner transform algorithms, in terms of parameter extraction accuracy, computational complexity, and execution time. Moreover, the implementation of a field programmable gate array accelerates SLVT computing by a factor of 116 in comparison to the CPU (Central Processing Unit) platform.

A comparative study of analytical models of diffuse reflectance in homogeneous biological tissues: Gelatin-based phantoms and Monte Carlo experiments.

Information about tissue oxygen saturation (StO2) and other related important physiological parameters can be extracted from diffuse reflectance spectra measured through non-contact imaging. Three analytical optical reflectance models for homogeneous, semi-infinite, tissue have been proposed (Modified Beer-Lambert, Jacques 1999, Yudovsky 2009) but these have not been directly compared for tissue parameter extraction purposes. We compare these analytical models using Monte Carlo (MC) simulated diffuse reflectance spectra and controlled gelatin-based phantoms with measured diffuse reflectance spectra and known ground truth composition parameters. The Yudovsky model performed best against MC simulations and measured spectra of tissue phantoms in terms of goodness of fit and parameter extraction accuracy followed closely by Jacques' model. In this study, Yudovsky's model appeared most robust; however, our results demonstrated that both Yudovsky and Jacques models are suitable for modeling tissue that can be approximated as a single, homogeneous, semi-infinite slab.

Branch architecture quantification of large-scale coniferous forest plots using UAV-LiDAR data

Branch architecture plays an important role in forest physical and ecological processes, greatly affecting forest spatial structure and the accumulation, production and distribution of organic carbon. Very few studies have quantified the branch architecture of large-scale forest plots, due to the lack of high-resolution large-scale three-dimensional data. The emergence of unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) provides great potential to overcome this limitation. However, due to insufficient quality of UAV-LiDAR data for branch reconstruction, existing algorithms remain tremendously challenging. We propose an effective branch reconstruction algorithm for UAV-LiDAR data to quantify branch architecture. First, individual branches are extracted using a region growth method that is guided by the branch growth direction and local smoothness constraint. Second, incorrect branches are eliminated based on three pieces of branch features, i.e., specific angles between branches at the same whorl, specific height differences between branches at different whorls, and the growth pattern of branches from the stem outward, reaching maximum distances at tips. Finally, branches are reconstructed using polynomial fitting, and branch architecture parameters are extracted based on branch models. The proposed algorithm simplifies branch reconstruction by skipping the separation of photosynthetic and non-photosynthetic components. It also adapts well to UAV-LiDAR point clouds, generating more realistic reconstructions. The algorithm successfully identified non-photosynthetic components in experiments involving 240 trees, including Scots pine, Norway spruce, and Radiata pine. The average overall accuracy for these three species was 0.88, 0.76, and 0.74, respectively. The proposed algorithm was tested for branch reconstruction using UAV-LiDAR data from a two-hectare Larix principis-rupprechtii plot. The accuracy of branch identification and parameter extraction accuracy were evaluated branch by branch based on the individual branches manually identified in terrestrial laser scanning data. Results showed the F-score of branch and stem identification was 0.58 and 1. The relative RMSE of branch length and angle was 36.87% and 18.3%, and ones of stem length and diameter were 1.46% and 6.16%. The proposed algorithm outperformed the well-known reconstruction algorithms, including TreeQSM, AdTree and Laplacian-based algorithms.

Decoupling and Parameter Extraction Methods for Conical Micro-Motion Object Based on FMCW Lidar.

Micro-Doppler time-frequency analysis has been regarded as an important parameter extraction method for conical micro-motion objects. However, the micro-Doppler effect caused by micro-motion can modulate the frequency of lidar echo, leading to coupling between structure and micro-motion parameters. Therefore, it is difficult to extract parameters for micro-motion cones. We propose a new method for parameter extraction by combining the range profile of a micro-motion cone and the micro-Doppler time-frequency spectrum. This method can effectively decouple and accurately extract the structure and the micro-motion parameters of cones. Compared with traditional time-frequency analysis methods, the accuracy of parameter extraction is higher, and the information is richer. Firstly, the range profile of the micro-motion cone was obtained by using an FMCW (Frequency Modulated Continuous Wave) lidar based on simulation. Secondly, quantitative analysis was conducted on the edge features of the range profile and the micro-Doppler time-frequency spectrum. Finally, the parameters of the micro-motion cone were extracted based on the proposed decoupling parameter extraction method. The results show that our method can effectively extract the cone height, the base radius, the precession angle, the spin frequency, and the gravity center height within the range of a lidar LOS (line of sight) angle from 20° to 65°. The average absolute percentage error can reach below 10%. The method proposed in this paper not only enriches the detection information regarding micro-motion cones, but also improves the accuracy of parameter extraction and establishes a foundation for classification and recognition. It provides a new technical approach for laser micro-Doppler detection in accurate recognition.

Parameter extraction of photovoltaic model based on butterfly optimization algorithm with chaos learning strategy

Accurate extraction of photovoltaic (PV) model parameter is significant for the evaluation of PV system. Most of existing methods for extracting the PV parameters suffer from low extraction accuracy and are prone to fall into local optima. To solve these shortcomings, a chaos learning butterfly optimization algorithm (CLBOA) is proposed. First, the introduced Cauchy mutation increases the perturbation to butterflies and helps the algorithm to jump out of local optima. Second, a chaos learning strategy is proposed to lead the butterflies within the population to learn from the optimal individual using the improved Tent chaos mapping, which strengthens the learning exchange of the population, enhances the ability of global exploration and local exploitation, and optimizes the convergence speed and accuracy. Third, the three worst individual positions were randomly initialized using a terminal elimination mechanism to increase population diversity. After evaluating CLBOA through the CEC2022 benchmark suite, it was used to extract parameters for a total of 12 PV cell and module models for 5 materials. Compared with 9 well-known algorithms, CLBOA performs excellent in terms of convergence performance, parameters extraction accuracy, running time and improvement index. Finally, CLBOA was applied on YL PV power station model of Guizhou Power Grid in China under different irradiations and temperatures. The I-V and P-V curves reconstructed by CLBOA are highly fit to the synthesized curves, and the individual absolute error (IAE) and relative error (RE) also confirm the satisfactory accuracy of parameters extraction. Comprehensive analysis shows that CLBOA can extract parameters better than the comparison algorithms, demonstrating its strong potential for PV parameters extraction.

Enhanced Extraction of Activation Time and Contractility From Myocardial Strain Data Using Parameter Space Features and Computational Simulations.

A computational model enables the extraction of two critical myocardial tissue properties: activation time (AT) and contractility (Con) from recorded cardiac strains. However, interference between these parameters reduces the precision and accuracy of the extraction process. This study investigates whether leveraging features in the parameter space can enhance parameter extraction. We utilized a computational model to simulate sarcomere mechanics, creating a parameter space grid of 41 × 41 AT and Con pairs. Each pair generated a simulated strain pattern, and by scanning the grid, we identified cohorts of similar strain patterns for each simulation. These cohorts were represented as binary images-synthetic fingerprints-where the position and shape of each blob indicated extraction uniqueness. We also generated a measurement fingerprint for a strain pattern from a patient with left bundle branch block and compared it to the synthetic fingerprints to calculate a proximity map based on their similarity. This approach allowed us to extract AT and Con using both the measurement fingerprint and the proximity map, corresponding to simple optimization and enhanced parameter extraction methods, respectively. Each synthetic fingerprint consisted of a single connected blob whose size and shape varied characteristically within the parameter space. The AT values extracted from the measurement fingerprint and the proximity map ranged from -59 to 19 ms and from -16 to 14 ms, respectively, while Con values ranged from 48% to 110% and from 85% to 110%, respectively. This study demonstrates that similarity in simulations leads to an asymmetric distribution of parameter values in the parameter space. By using a proximity map, this distortion is considered, significantly improving the accuracy of parameter extraction.

A photovoltaic parameter identification method based on Pontogammarus maeoticus swarm optimization

Currently, the improvement of model parameter extraction accuracy is essential to research photovoltaic (PV) fields. In this study, a model parameter identification based on Pontogammarus maeoticus swarm optimization (PMSO) is proposed. The PMSO is used for parameter identification of mathematical models for PV modules. In the PMSO algorithm, by giving the ability of free exploration to particles that are far away from the optimal solution, the search scope is expanded to avoid falling into the local optimum. Besides, the local search for each Gammarus has a better convergence for PV parameter identification. Therefore, the accuracy of parameter identification for modeling PV modules is improved. The feasibility and superiority of the proposed method are verified by measured I-V characteristics of the PV array. The experimental results and error analysis verify that when compared with the conventional meta-heuristic algorithms, the proposed method achieves higher modeling accuracy. The proposed PMSO algorithm is suitable for engineering application of parameter identification and modeling of PV modules.

Extraction of Single Diode Model Parameters of Solar Cells and PV Modules by Combining an Intelligent Optimization Algorithm with Simplified Explicit Equation Based on Lambert W Function

In this paper, the explicit equation of the single diode model (SDM) expressed by the Lambert W function was reduced to its simplified form through variable replacement; then the simplified explicit equation was combined with an intelligent optimization algorithm to estimate the SDM parameters of solar cells and PV modules. To evaluate the parameter extraction performance of the new method, eight typical intelligent optimization algorithms were combined with the implicit, explicit, and simplified explicit equation to extract the SDM parameters of a solar cell and three types of PV modules. The results show that the new method not only improves the accuracy of parameter extraction but also enhances the robustness and convergence speed. Most importantly, the new method can nearly improve the parameter extraction accuracy of a poor-performing algorithm in traditional methods to the level of other well-performing algorithms without enhancing the algorithm itself. In a word, this study offers a new choice for a more accurate and reliable extraction of SDM parameters from both solar cells and PV modules.

The Fornax Deep Survey with the VST

Context.Low surface brightness (LSB) dwarf galaxies in galaxy clusters are an interesting group of objects as their contribution to the galaxy luminosity function and their evolutionary paths are not yet clear. Increasing the completeness of our galaxy catalogs is crucial for understanding these galaxies, which have effective surface brightnesses below 23 mag arcsec−2(in optical). Progress is continuously being made via the performance of deep observations, but detection depth and the quantification of the completeness can also be improved via the application of novel approaches in object detection. For example, the Fornax Deep Survey (FDS) has revealed many faint galaxies that can be visually detected from the images down to a surface brightness level of 27 mag arcsec−2, whereas traditional detection methods, such as using Source Extractor (SE), fail to find them.Aims.In this work we use a max-tree based object detection algorithm (Max-Tree Objects, MTO) on the FDS data in order to detect previously undetected LSB galaxies. After extending the existing Fornax dwarf galaxy catalogs with this sample, our goal is to understand the evolution of LSB dwarfs in the cluster. We also study the contribution of the newly detected galaxies to the faint end of the luminosity function.Methods.We test the detection completeness and parameter extraction accuracy of MTO using simulated and real images. We then apply MTO to the FDS images to identify LSB candidates. The identified objects are fitted with 2D Sérsic models using GALFIT and classified as imaging artifacts, likely cluster members, or background galaxies based on their morphological appearance, colors, and structure.Results.With MTO, we are able to increase the completeness of our earlier FDS dwarf catalog (FDSDC) 0.5–1 mag deeper in terms of total magnitude and surface brightness. Due to the increased accuracy in measuring sizes of the detected objects, we also add many small galaxies to the catalog that were previously excluded as their outer parts had been missed in detection. We detect 265 new LSB dwarf galaxies in the Fornax cluster, which increases the total number of known dwarfs in Fornax to 821. Using the whole cluster dwarf galaxy population, we show that the luminosity function has a faint-end slope ofα= −1.38 ± 0.02. We compare the obtained luminosity function with different environments studied earlier using deep data but do not find any significant differences. On the other hand, the Fornax-like simulated clusters in the IllustrisTNG cosmological simulation have shallower slopes than found in the observational data. We also find several trends in the galaxy colors, structure, and morphology that support the idea that the number of LSB galaxies is higher in the cluster center due to tidal forces and the age dimming of the stellar populations. The same result also holds for the subgroup of large LSB galaxies, so-called ultra-diffuse galaxies.

Comparing Mobile Laser Scanner and manual measurements for dendrometric variables estimation in a black pine (Pinus nigra Arn.) plantation

The growing demand of ecosystem services provided by forests increased the need for fast and accurate field survey. The recent technological innovations fostered the application of geomatic tools and processes to different fields of the forestry sector. In this study we compared the efficiency and the accuracy of Mobile Laser Scanner (MLS), combined with Simultaneous Localization and Mapping (SLAM) technology, and traditional field survey for the mensuration of main forest dendrometric variables like stem diameter at breast height (DBH), individual tree height (H), crown base height (CBH) and branch-free stem volume (VOL). With ground truth measurements taken from 50 felled trees, we tested the applicability of MLS technology for individual tree parameters estimation in a conifer plantation in central Italy. Our results showed no bias of DBH estimates and the corresponding RMSE was equal to 10.8% (2.7 cm). H and CBH measured with MLS were underestimated compared to the ground truth (bias of −8.6% for H and −13.3% for CBH). VOL values showed a bias and a RMSE of −4.1% (−0.01 m3) and 12.4% (0.04 m3) respectively. Tree height is not perfectly estimated due to laser obstruction by crowns layer, but the acquisition speed of this survey, joined with a suitable accuracy of parameters extraction, suggests sufficient suitability of the method for operational applications in simple forest structures (e.g. one-layered stands).

Fatigue detection based on facial feature correction and fusion

Fatigue driving has always been a major hidden danger that threatens traffic safety. The introduction of fatigue monitoring technology can greatly reduce traffic accidents caused by fatigue driving and produce huge social effects. At present, in the field of vision-based fatigue driving research, the extraction of the driver’s eye or mouth features is often used for judgment. However, the criterion is single and the post-processing is insufficient, and the interference of factors such as noise can easily cause misjudgement and reduce the recognition rate. In order to improve the accuracy of parameter extraction and solve the problem of low recognition rate of a single parameter, this paper proposes a feature extraction algorithm based on Euler angle correction, and adds a multi-feature fusion decision method to enhance the applicability of the algorithm. Experiments prove that the algorithm has higher accuracy after modification and fusion.

A comparative study on the accuracy of small-signal equivalent circuit modeling for large gate periphery GaN HEMT with different source to drain length and gate width

In this paper, extrinsic and intrinsic parameters were extracted from the experimental S-parameters using 16- and 22-element small signal equivalent circuit model for large gate periphery GaN HEMT with different source to drain length and gate width over 500 MHz to 26 GHz range. The extraction using 22-element model shows good consistency to measured S-parameters and the maximum gain, stability factor, delta factor and K-factor with less percentage error than 16-element model. As the source to drain length is increasing, the accuracy of parameter extraction is enhanced for 22-element model. Specially for stability factor and K-factor the percentage error was reduced drastically (10.42%–4.74% and 8.13%–0.83% respectively). For increased number of fingers, the 22-element model shows more accuracy. Also, TCAD (Technology Computer-Aided Design) device simulations were done and transfer characteristic were calibrated with measured data. The simulated s-parameters were then calibrated with the experimental extrinsic parameters.

Extreme learning machine based meta-heuristic algorithms for parameter extraction of solid oxide fuel cells

A precise, fast, and robust parameter extraction technique of solid oxide fuel cell models is extremely crucial for optimal control and behavior analysis. In this paper, a novel extreme learning machine based method is proposed to extract unknown parameters of solid oxide fuel cell models including electrochemical model and simple electrochemical model. At first, extreme learning machine is applied to overcome two thorny obstacles (e.g., data shortage and noised data) via predicting additional data and updating noised data. Then, both original data collected from a 5 kW solid oxide fuel cell stack and processed data are transferred to effectively guide eight prominent meta-heuristic algorithms for effective parameter extraction. The performance of extreme learning machine is thoroughly investigated in two typical operation conditions through a comprehensive comparison based on various training data. Simulation results validate that the proposed approach can effectively contribute to searching efficient model parameters along with high accuracy, prominent stability, high speed, and great robustness. Particularly, the accuracy of parameter extraction for electrochemical model and simple electrochemical model can be improved by 49.3% and 65.6% at most, respectively.

Delay-Doppler Robust Spectrum Sharing of UAV and Terrestrial Systems Aided by Assistive Slots

A delay-Doppler robust spectrum sharing method is proposed for the unmanned aerial vehicle (UAV) and terrestrial systems by actively introducing the assistive slots (AS) in this work. With the aids of AS, the receiver in the UAV/terrestrial link can extract the delay-Doppler parameter of the signal, and hence further recover the desired signal. Inserting AS in frames introduces different possibilities of signals sampled at AS and non-AS points, and the UAV/terrestrial signal samples are differentiated from the compound signals by focusing on the energy gap among the samples. Since the signal sample differentiation and the delay-Doppler parameter extraction are receiver oriented, the effects of multipath and node mobility are included, and the signal recovery is free from the adverse impacts of delay and Doppler shifts. Analysis shows that although introducing AS is beneficial to recovering the UAV/terrestrial signal, the signal transmission efficiency suffers degradation. Therefore, the optimal AS ratio regarding the tradeoff between delay-Doppler parameter extraction accuracy and transmission efficiency is investigated. For the optimal AS ratio, the signal-to-interference ratio of the spectrum sharing system plays a key role under Rician/Rayleigh distributed terrestrial fading channels. The numerical results validate the effectiveness of the proposed scheme in comparison to the traditional methods.

Self‐learning tuning of coaxial cavity filter by using the poles and residues of admittance function

To solve the complexity and blindness of the tuning process in the manufacture of microwave cavity filters, this study proposes a self-learning tuning method based on the poles and residues of the admittance function. First, the improved Cauchy's method based on the differential evolution algorithm is used to extract the poles and residues of the admittance parameters (Y-parameters) in a non-ideal environment, and the effect of different port phase shifts and cavity losses on the accuracy of parameter extraction is overcome. Second, a parametric model based on the experience and data fusion via the fuzzy neural network method is established according to the collected non-linear relation data. Furthermore, problems such as poor data reliability, low modelling accuracy and weak generalisation ability are solved. On this basis, an adaptive optimisation tuning of microwave cavity filters using an implicit space-mapping algorithm is proposed and problems such as convergence difficulty and dependence on the initial value are solved. The results of the online simulation show that the proposed method has a high tuning accuracy and fast tuning ability.

An Accurate Neural Network-Based Consistent Gate Charge Model for GaN HEMTs by Refining Intrinsic Capacitances

Neural network-based capacitance models are accurate, but some of them are not charge-conservative. In this work, a novel consistent gate charge model for GaN high electron mobility transistors is presented based on neural networks. The equivalent circuit parameters are extracted using the multiobjective gray wolf optimizer-based hybrid method, which improves the accuracy of parameter extraction. To obtain more reliable data sets for accurate neural network-based modeling, the outliers in the extracted intrinsic capacitances are automatically detected and removed using the isolation forest technique. The gate charge is obtained by integrating the capacitances with the voltages at different temperatures. A neural network is used to model the bias- and temperature-dependent gate charges, and the intrinsic capacitance formulation is obtained by taking the partial derivative of the gate charge function with respect to the voltages. The proposed model is charge-conservative and requires no transcapacitances. The large-signal model is implemented in the Advanced Design System and verified by small- and large-signal measurements. Good agreement is obtained between the measurements and simulations.

Accelerating Parameter Extraction of PSP MOSFET Model on SoC Platform

In this paper, a novel approach to accelerate parameter extraction process of surface-potential-based (PSP) MOSFET model is presented for the submicron MOS transistor. To reduce the computational time, Field Programmable Gate Array (FPGA) implementation of PSP model library — SiMKit is demonstrated using Xilinx’s Zynq [Formula: see text] (Multi-processor System-on-Chip) platform. Parameter extraction is carried out using Particle Swarm Optimization (PSO) algorithm for 65[Formula: see text]nm technology nMOS devices. With the available measurement data, 32 various PSP model parameters are extracted. Experimental results validate the performance and accuracy of parameter extraction by achieving Root Mean Square Error below 10% for various current–voltage characteristics of nMOS device. 41.57% acceleration in execution time for extraction process is achieved by Zynq [Formula: see text] platform compared to the conventional computer-based software approach. In addition, various design optimization directives are explored, and their performances are compared as a part of RTL generation of SiMKit.

Forest 3D Reconstruction and Individual Tree Parameter Extraction Combining Close-Range Photo Enhancement and Feature Matching

An efficient and accurate forest sample plot survey is of great significance to understand the current status of forest resources at the stand or regional scale and the basis of scientific forest management. Close-range photogrammetry (CRP) technology can easily and quickly collect sequence images with high overlapping to reconstruct the 3D model of forest scenes and extract the individual tree parameters automatically and, therefore, can greatly improve the efficiency of forest investigation and has great application potential in forestry visualization management. However, it has some issues in practical forestry applications. First, the imaging quality is affected by the illumination in the forest, resulting in difficulty in feature matching and low accuracy of parameter extraction. Second, the efficiency of 3D forest model reconstruction is limited under complex understory vegetation or the topographic situation in the forest. In addition, the density of point clouds by dense matching directly affects the accuracy of individual tree parameter extraction. This research collected the sequence images of sample plots of four tree species by smartphones in Gaofeng Forest Farm in Guangxi and Wangyedian Forest Farm in Mongolia to analyze the effects of image enhancement, feature detection and dense point cloud algorithms on the efficiency of 3D forest reconstruction and accuracy of individual tree parameter extraction, then proposed a strategy of 3D reconstruction and parameter extraction suitable for different forest scenes. First, we compared the image enhancement effects of median–Gaussian (MG) filtering, single-scale retinex (SSR) and multi-scale retinex (MSR) filtering algorithms. Then, an improved algorithm combining Harris corner detection with speeded-up robust features (SURF) feature detection (Harris+SURF) is proposed, and the feature matching effect is compared with that of a scale invariant feature transform (SIFT) operator. Third, according to the morphological characteristics of the trees in the sequence images, we used the iterative interpolation algorithm of a planar triangulation network based on geometric constraints (GC-based IIPTN) to increase the density of point clouds and reconstruct the 3D forest model, and then extract the position and DBH of the individual trees. The results show that MSR image enhancement can significantly increase the number of matched point pairs. The improved Harris+SURF method can reduce the reconstruction time of the 3D forest model, and the GC-based IIPTN algorithm can improve the accuracy of individual tree parameter extraction. The extracted position of the individual tree is the same as the measured position with the bias within 0.2 m. The accuracy of extracted DBH of Eucalyptus grandis, Taxus chinensis, Larix gmelinii and Pinus tabuliformis is 94%, 95%, 96% and 90%, respectively, which proves that the proposed 3D model reconstruction method based on image enhancement has great potential for tree position and DBH extraction, and also provides effective support for forest resource investigation and visualization management in the future.

A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR

Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction.

Parameter extraction of PEMFC via Bayesian regularization neural network based meta-heuristic algorithms

It is essential to establish an accurate model for precise and reliable evaluation of the characteristics of proton exchange membrane fuel cell (PEMFC). However, the inherent multi-variable, multi-peak, and nonlinear features of PEMFC seriously increase the difficulty and complexity of its parameter extraction. Besides, noised data, which is inevitable in various operation conditions, usually hinders meta-heuristic algorithms (MhAs) to obtain high-quality PEMFC parameters. For the sake of solving these obstacles, a Bayesian regularized neural network (BRNN) based parameter extraction strategy of PEMFC is proposed. Furthermore, performance of the proposed approach is thoroughly evaluated and analyzed through a comprehensive comparison with several advanced MhAs under various operation conditions. Lastly, simulation results verified that BRNN based MhAs (BRNN-MhAs) can effectively extract the parameters of PEMFC with higher accuracy, faster speed, and enhanced stability. In particular, the accuracy of parameter extraction of PEMFC is growing by 34.18%.