Gaussian Radial Basis Function Research Articles

Analysis of influencing factors of traffic accidents on urban ring road based on the SVM model optimized by Bayesian method.

Based on small scale sample of accident data from specific scenarios, fully exploring the potential influencing factors of the severity of traffic accidents has become a key and effective research method. In order to analyze the factors mentioned above in the scenario of urban ring roads, this paper collected data records of 1250 traffic accidents involving different severity on urban ring road of a central city in northwest China in the past 3 years. Firstly, the Support Vector Machine (SVM) model of non-parametric method is utilized to analyze the data above, and three kernel functions of linear, inhomogeneous polynomial and Gaussian radial basis are constructed respectively. Considering comprehensively 16 potential influencing factors covering the driver-vehicle-road-environment integrated system, the SVM models of above three kernel functions are verified, accuracy reaches 0.771 and F1 reaches 0.841. Then, Bayesian Optimization (BO), Grids Search (GS) and Rough Set (RS) are utilized as optimizer to adjust the parameters of Gaussian radial basis function SVM model, the performance of BO-SVM is further improved and reaches the optimum, with an average accuracy of 0.875 on the test set and a F1 of 0.886, completely outperforming the benchmark models of GS-SVM, RS-SVM, Bilayer-LSTM and BP. Finally, the sensitivity analysis method is utilized to quantify the sensitivity of the potential influencing factors to the severity of road accidents, and the backward selection method is utilized to screen the core influencing factors that influence the severity of accident, concluded that core influencing factors are age, driving mileage and vehicle type. This paper will provide reference for the analysis of the significant influencing factors for road accidents severity, and to provide theoretical support for the precise formulation of accident improvement strategies.

Performance evaluation of housing price index prediction model using DNN and SVR

This study used the base interest rate, mortgage interest rate, deposit interest rate, consumer price index, exchange rate, and composite stock price index as variables to predict the housing price index. The variables were normalized using the min-max normalization method. Machine learning methods, Deep Neural Networks (DNN) and Support Vector Regression (SVR), were employed to construct a prediction model, and the fit of the model was evaluated using the Root Mean Square Error (RMSE). The analysis results of the DNN model indicated that when applying the softplus activation function, the RMSE was 0.1298, resulting in the smallest prediction error, and the correlation coefficient between the predicted and actual values was highest at 0.9342. The analysis results of the SVR model using the Gaussian RBF kernel showed that the RMSE was smallest at 0.2781 when the parameters were set to C=100 and Gamma=0.1. Therefore, the DNN model was found to be more fit for predicting the housing price index. Additionally, it was confirmed that the consumer price index had a non-linear effect with a positive relationship, and the base interest rate had a linear effect with a negative relationship.

Investigating the missing-wedge problem in small-angle X-ray scattering tensor tomography across real and reciprocal space.

Small-angle-scattering tensor tomography is a technique for studying anisotropic nanostructures of millimetre-sized samples in a volume-resolved manner. It requires the acquisition of data through repeated tomographic rotations about an axis which is subjected to a series of tilts. The tilt that can be achieved with a typical setup is geometrically constrained, which leads to limits in the set of directions from which the different parts of the reciprocal space map can be probed. Here, we characterize the impact of this limitation on reconstructions in terms of the missing wedge problem of tomography, by treating the problem of tensor tomography as the reconstruction of a three-dimensional field of functions on the unit sphere, represented by a grid of Gaussian radial basis functions. We then devise an acquisition scheme to obtain complete data by remounting the sample, which we apply to a sample of human trabecular bone. Performing tensor tomographic reconstructions of limited data sets as well as the complete data set, we further investigate and validate the missing wedge problem by investigating reconstruction errors due to data incompleteness across both real and reciprocal space. Finally, we carry out an analysis of orientations and derived scalar quantities, to quantify the impact of this missing wedge problem on a typical tensor tomographic analysis. We conclude that the effects of data incompleteness are consistent with the predicted impact of the missing wedge problem, and that the impact on tensor tomographic analysis is appreciable but limited, especially if precautions are taken. In particular, there is only limited impact on the means and relative anisotropies of the reconstructed reciprocal space maps.

Practical time‐varying formation cooperative control for high‐order nonlinear multi‐agent systems avoiding spatial resource conflict via safety constraints

AbstractThe operation of multi‐agent systems (MAS) in space necessitates considerations for obstacle avoidance, collision prevention, and connectivity among agents. This coupling conflict, stemming from spatial resource utilization, poses a significant and non‐negligible threat to the safe operation of MAS. This article proposes a consensus control scheme for the time‐varying formation of MAS, incorporating functions for maintaining connectivity, collision avoidance, and obstacle dodging. This scheme effectively constrains the time‐varying formation tracking error within an arbitrarily small range. The considered system model takes a high‐order nonlinear form, with uncertainties and disturbances present in each order. This grants the control scheme with high generality and practicability. By employing barrier Lyapunov function to delineate the safe operational space of MAS, conflicts in spatial resource utilization are avoided. This approach simultaneously fulfills the requirements for connectivity maintenance, collision avoidance, and obstacle dodging in the safe operation of MAS. An additional rotation operator is integrated into the controller to smoothly address the “minima” problem, eliminating the need for external intervention. Gaussian radial basis function is used to estimate the nonlinear terms, uncertainties, and unknown perturbation online in the system. The stability of the MAS under the proposed control scheme is analyzed through Lyapunov function. Finally, numerical simulation results are demonstrated to explain the effectiveness of the control scheme.

Robust baseline correction for Raman spectra by constrained Gaussian radial basis function fitting

Accurate baseline correction is a fundamental requirement for extracting meaningful spectral information and enabling precise quantitative analysis using Raman spectroscopy. Although numerous baseline correction techniques have been developed, they often require meticulous parameter adjustments and yield inconsistent results. To address these challenges, we have introduced a novel approach, namely constrained Gaussian radial basis function fitting (CGF). Our method involves solving a curve-fitting problem using Gaussian radial basis functions under specific constraints. To ensure stability and efficiency, we developed a linear programming algorithm for the proposed approach. We evaluated the performance of CGF using simulated Raman spectra and demonstrated its robustness across various scenarios, including changes in data length and noise levels. In contrast to standard methods, which frequently require complicated parameter adjustments and may exhibit varying errors, our approach provides a simple parameter search and consistently achieves low errors. We further assessed CGF using real Raman spectra, leading to enhanced accuracy in the quantitative analysis of the Raman spectra of chemical warfare agents. Our results emphasize the potential of CGF as a valuable tool for Raman spectroscopy data analysis, significantly advancing sophisticated analytical techniques.

A stable localized weak strong form radial basis function method for modelling variably saturated groundwater flow induced by pumping and injection

The unsaturated zone profoundly affects groundwater (GW) flow induced by pumping and injection due to the capillary forces. However, current radial basis function (RBF) numerical models for GW pumping and injection mostly ignore the unsaturated zone. To bridge this gap, we developed a new three-dimensional weak strong form RBF model in this study, called CCHE3D-GW-RBF. Flow processes were modelled by the mixed-form Richards equation which was iteratively solved by the modified Picard iteration. Soil-water characteristic curves were represented by the widely applicable formulas, the van Genuchten (1980) model. Differential operators were approximated by the localized Gaussian RBF, and the weighted singular value decomposition method was used to construct stable bases. The injection/pumping wells and the flux boundaries were handled by the weak formulation using Meshless Local Petrov Galerkin method, and the strong-form equation using the collocation RBF method was enforced on the other points. Good agreement was found between the simulation results from our numerical model and the well-accepted solutions in all three verification cases, demonstrating the accuracy and applicability of this model. In addition, a smaller RBF shape parameter was found to produce a more accurate modelling resulting, indicating the necessity of implementing stable bases for RBF models.

2D temperature field reconstruction using optimized Gaussian radial basis function networks

Monitoring temperature fields by sensors and mapping the reconstructed temperature field holds great importance in numerous contexts. Concurrently, as sensing technologies continue to advance, there is a parallel development of temperature reconstruction algorithms. In this study, we present an enhanced Quadratic Optimization Gaussian Radial Basis Function approximation (QO-GRBF). We demonstrate the effectiveness of this algorithm by reconstructing a 10 cm x 10 cm temperature field using distributed optical fiber sensing system. The absolute error across four different temperature stages from 35 °C to 65 °C ranged from 0.21 °C to 0.45 °C, indicating a strong reconstruction ability. Furthermore, we compared the performance of QO-GRBF with GRBF in different point densities. The results reveal an over 30 % improvement in scenarios with denser data points, highlighting the efficacy of proposed algorithm. The data size chosen was quantified as well. There was a 10.22 % improvement observed when transitioning from selecting 4 points to selecting 6 points.

Robust and comprehensive predictive models for methane hydrate formation condition in the presence of brines using black-box and white-box intelligent techniques

Accurate estimation of gas hydrate formation condition is crucial for many reasons. In one hand, the gas hydrate formation is a promising approach for gas separation, cold energy storage, seawater desalination, etc. On the other hand, in some industries, this phenomenon may cause some challenges, such as pipelines blockage. The current study was undertaken to develop trustworthy predictive tools for methane hydrate formation temperature (MHFT) in the presence of brines. To achieve this target, 1051 experimental data pertinent to the methane hydrate equilibrium in 26 single and multiple brines over an extensive range of operating conditions were amassed from published studies. Four simple factors, including pressure, brine concentration, salt molecular weight and salt melting temperature were defined as the input variables, and the black-box intelligent techniques, including gaussian process regression (GPR), multilayer perceptron (MLP) and radial basis function (RBF) were employed to link MHFT to the mentioned factors. A plethora of graphical and statistically-based assessments were performed to determine the accuracy level of the proposed models. While all intelligent models had excellent performances, the one established by the GPR method showed the best agreements with experimental data, and gave the AARE and R2 values of 0.14% and 99.17%, respectively, in the testing step. The foregoing model was capable of predicting MHFT over a broad range of conditions with relative deviations mostly below 0.10%. Additionally, it showed an excellent performance in describing the physical variations of MHFT versus operating factors. The authority of the analyzed databank and the presented model was asserted via the William's plot investigation. Moreover, a sensitivity analysis was undertaken in order to determine the most fundamental factors in controlling MHFT. Eventually, a simple mathematical correlation was also derived based on the white-box intelligent approach of gene expression programming (GEP), which allowed the accurate estimation of MHFT with an AARE of 0.56%.

Kernel Block Based Morphing Identification and Elimination In Server

The primary stage of duplicated region identification for investigating duplicate and insert image forgeries does the image block match. Because of evolution of highly sophisticated tools, numerous image modifications have been made. When images are difficult to duplicate and transfer, forgeries of those parts may happen. The images are examined closely in the area where they were forged. By using the suggested Gaussian RBF kernel PCA, the segment of the image that is copied and pasted will be known. One of the most significant issues in this step is the high processing time required to locate related locations. This research proposes a digital block-based picture water mark embedding strategy this uses singular value decomposition (SVD), a mathematical technique. There is already embedding a water mark across the entire image using traditional SVD watermarking. The suggested method involves breaking up the partitioning original image into blocks, after which the water mark existindividually inserted into the singular value of each block. Additionally, the employ the temporal complexity calculate the recommended algorithm's performance. The result of the experiment and thecalculation analysisdemonstrate that two layer matching is possible more traditional approaches Similar to lexicographic sorting, illustrate how two-layer matching can be accomplished. . High key points are awarded for the dimensionality of the feature vector representation in the picture matching. The suggested approach eliminates compression, identifies picture features using blurring, and contaminates the image with noise. The most straightforward way to identify image forgeries using editing technology or morphing is through computational efficiency. Numerous tests have shown that the system is more efficient than the one that is already in use. It is helpful for recording historical time intervals with patterns resembling those of a query time range. To obtain an accurate prediction, the system determines the location, MAC address, and IP address of the morphing uploader.

Reliable smart models for estimating frictional pressure drop in two-phase condensation through smooth channels of varying sizes

Reliable and comprehensive predictive tools for the frictional pressure drop (FPD) are of particular importance for systems involving two-phase flow condensation. However, the available models are only applicable to specific operating conditions and channel sizes. Thus, this study aims at developing universal models to estimate the FPD during condensation inside smooth mini/micro and conventional (macro) channels. An extensive databank, comprising 8037 experimental samples and 23 working fluids from 50 reliable sources, was prepared to achieve this target. A comprehensive investigation on the literature models reflected the fact that all of them are associated with high deviations, and their average absolute relative errors (AAREs) exceed 26%. Hence, after identifying the most effective input variables through the Spearman's correlation analysis, three soft-computing paradigms, i.e., multilayer perceptron (MLP), gaussian process regression (GPR) and radial basis function (RBF) were employed to establish intelligent and dimensionless predictive tools for the FPD based on the separated model suggested by Lockhart and Martinelli. Among them, the most accurate results were presented by the GPR approach with AARE and R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^{2}$$\\end{document} values of 4.10%, 99.23% respectively, in the testing step. The truthfulness and applicability of the models were explored through an array of statistical and visual analyses, and the results affirmed the obvious superiority of the newly proposed approaches over the literature correlations. Furthermore, the novel predictive tools excellently described the physical variations of the condensation FPD versus the operating parameters. Ultimately, the order of importance of factors in controlling the condensation FPD was clarified by a sensitivity analysis.

Ricci flow-based brain surface covariance descriptors for diagnosing Alzheimer’s disease

Automated feature extraction from MRI brain scans and diagnosis of Alzheimer’s disease are ongoing challenges. With advances in 3D imaging technology, 3D data acquisition is becoming more viable and efficient than its 2D counterpart. Rather than using feature-based vectors, in this paper, for the first time, we suggest a pipeline to extract novel covariance-based descriptors from the cortical surface using the Ricci energy optimization. The covariance descriptors are components of the nonlinear manifold of symmetric positive-definite matrices, thus we focus on using the Gaussian radial basis function to apply manifold-based classification to the 3D shape problem. Applying this novel signature to the analysis of abnormal cortical brain morphometry allows for diagnosing Alzheimer’s disease. Experimental studies performed on about two hundred 3D MRI brain models, gathered from Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate the effectiveness of our descriptors in achieving remarkable classification accuracy.

Machine Learning Application of Generalized Gaussian Radial Basis Function and Its Reproducing Kernel Theory

Gaussian Radial Basis Function Kernels are the most-often-employed kernel function in artificial intelligence for providing the optimal results in contrast to their respective counterparts. However, our understanding surrounding the utilization of the Generalized Gaussian Radial Basis Function across different machine learning algorithms, such as kernel regression, support vector machines, and pattern recognition via neural networks is incomplete. The results delivered by the Generalized Gaussian Radial Basis Function Kernel in the previously mentioned applications remarkably outperforms those of the Gaussian Radial Basis Function Kernel, the Sigmoid function, and the ReLU function in terms of accuracy and misclassification. This article provides a concrete illustration of the utilization of the Generalized Gaussian Radial Basis Function Kernel as mentioned earlier. We also provide an explicit description of the reproducing kernel Hilbert space by embedding the Generalized Gaussian Radial Basis Function as an L2−measure, which is utilized in implementing the analysis support vector machine. Finally, we provide the conclusion that we draw from the empirical experiments considered in the manuscript along with the possible future directions in terms of spectral decomposition of the Generalized Gaussian Radial Basis Function.

Developing a novel image marker to predict the clinical outcome of neoadjuvant chemotherapy (NACT) for ovarian cancer patients

ObjectiveNeoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the clinical outcomes to NACT vary significantly among different subgroups. Partial responses to NACT may lead to suboptimal debulking surgery, which will result in adverse prognosis. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy prognosis prediction of NACT at an early stage. MethodsFor this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. This cluster was used as input for developing and optimizing support vector machine (SVM) based classifiers, which indicated the likelihood of receiving suboptimal cytoreduction after the NACT treatment. Two different kernels for SVM algorithm were explored and compared. A total of 42 ovarian cancer cases were retrospectively collected to validate the scheme. A nested leave-one-out cross-validation framework was adopted for model performance assessment. ResultsThe results demonstrated that the model with a Gaussian radial basis function kernel SVM yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.806 ± 0.078. Meanwhile, this model achieved overall accuracy (ACC) of 83.3%, positive predictive value (PPV) of 81.8%, and negative predictive value (NPV) of 83.9%. ConclusionThis study provides meaningful information for the development of radiomics based image markers in NACT treatment outcome prediction.

A method for detecting spatiotemporal patterns of cancer biomarkers-evoked activity using radial basis function network extracted time-domain features from calcium imaging data

BackgroundTwo-photon calcium imaging is widely used to study the odor-evoked glomerular activity in the dorsal olfactory bulb of macrosmatic animals. The nonstationary character of activated patterns sets a limit on the use of a traditional image processing approaches. New methodThe developed method makes it possible to automatically map cancer biomarkers-activated glomeruli in the rat dorsal olfactory bulb. We interpolated fluorescence intensity of calcium dynamics based on the Gaussian RBF network and synthesized the physiological fluorescence model of the receptive glomerular field. ResultsThe experiments on 5 rats confirmed the correctness of the developed approach. Patterns evoked by the 6-methyl-5-hepten-2-one (stomach cancer biomarker) and benzene (lung cancer biomarker) were correctly identified. Comparison with existing methodsThe proposed method was compared with the nonnegative matrix factorization method and with the method based on computer vision algorithms. The developed approach showed better accuracy in experiments and provided the mathematical models of the odor-evoked patterns synthesis. These models can be used to generate synthetic images of odor-evoked glomerular activity and thus to overcome the problem of small experimental data collected in calcium imaging. ConclusionsThe proposed method should be considered part of the toolkit for fully automatic analysis of calcium imaging-based studies. Currently available methodology is not able to use breath biomarkers to reliably discriminate between cancer patients and healthy controls. Nevertheless, the effective identification of the spatial patterns of cancer biomarkers-evoked glomerular activity can serve as the foundation for highly sensitive biohybrid systems for cancer screening.

Research on automatic detection of gradual fault of high voltage electric energy metering transformer based on fuzzy rough set and whale optimization algorithm

Based on fuzzy rough set and whale optimization algorithm, the automatic fault detection method of high-voltage electric energy metering transformer is studied to improve the fault diagnosis effect and efficiency. On the basis of constructing the mathematical model of gradual fault of high-voltage electric energy metering transformer, the fuzzy rough set theory is used to reduce the data attributes of fault samples, eliminate similar attributes, determine the minimum fault feature set, and complete the fault feature selection, which is used as the input of the fault detection model based on Whale Optimization Algorithm-based Support Vector Machine (WOA-SVM). After the kernel parameters and penalty factors of SVM are optimized by whale optimization algorithm, the type of gradual fault of high-voltage electric energy metering transformer is identified. The experimental results show that the reduced fault attributes are distributed differently in the sample data, and the fault detection accuracy can be improved by 9.5 % through fault feature selection. The fault diagnosis model with Gaussian radial basis function, kernel parameter of 0.05 and penalty factor of 10 has the best performance. This method can identify the gradual fault types of high-voltage electric energy metering transformers, and the fault diagnosis effect is outstanding.

Q-space imaging based on Gaussian radial basis function with Laplace regularization.

To introduce the diffusion signal characteristics presented by spherical harmonics (SH) basis into the q-space imaging method based on Gaussian radial basis function (GRBF) to robustly reconstruct ensemble average diffusion propagator (EAP) in diffusion MRI (dMRI). We introduced the Laplacian regularization of the signal into the dMRI imaging method based on GRBF, and derived the relevant indicators of microstructure imaging and the orientation distribution function (ODF) providing fiber bundle direction information based on EAP. In addition, this method is combined with a multi-compartment model to calculate the diameter of fiber bundle axons. The evaluation of the results included qualitative comparisons and quantitative assessments of the signal fitting. The results show that the proposed method achieves the more significant accuracy improvement in reconstructing signal. Meanwhile, ODFs estimated by the proposed method show the sharper profiles and less spurious peaks, even under the sparse and noisy conditions. In the 36 sets of axon diameter estimation experiments, 34 and 30 sets of results showed that the proposed method reduced the mean and SD of axon diameter estimates, respectively. Moreover, compared with the current state-of-the-art method, the mean and SD of axon diameter estimated by the proposed method are mostly lower, with 32 and 29 of 36 groups. The proposed method outperforms the GRBF regarding signal fitting and the estimation of the EAP and ODF with multi-shell sparse samples. Moreover, it shows the potential to recover important features of microstructures with less uncertainty by using proposed method together with multi-compartment models.

Gaussian RBF kernels via Fock spaces: quaternionic and several complex variables settings

In this paper, we study two extensions of the complex-valued Gaussian radial basis function (RBF) kernel and discuss their connections with Fock spaces in two different settings. First, we introduce the quaternionic Gaussian RBF kernel constructed using the theory of slice hyperholomorphic functions. Then, we consider the case of Gaussian RBF kernels in several complex variables.

Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine

Spatiotemporally mapping winter wheat is imperative for informing and shaping global food security policies. Traditional mapping methods heavily rely on sufficient and reliable samples obtained through labor-intensive fieldwork and manual sample collection. However, these methods are time-consuming, costly, and lack timely and continuous data collection. To address these challenges and fully leverage remote sensing big data and cloud computing platforms like Google Earth Engine (GEE), this paper developed an algorithm for Auto-Generating Winter Wheat Samples for mapping (AGWWS). The AGWWS utilizes historical samples to determine the optimal migration threshold by measuring Spectral Angle Distance (SAD), Euclidean Distance (ED), and Near-Infrared band Difference Index (NIRDI). This facilitates the auto-generation of winter wheat sample sets for the years 2000, 2005, 2010, 2015, and 2021. Approximately two-thirds of the samples were allocated for training, with the remaining one-third used for validating the mapping method, employing the One-Class Support Vector Machine (OCSVM). The Huang–Huai–Hai (HHH) Plain, a major winter wheat production region, was selected to perform the algorithm and subsequent analysis on. Different combinations of the hyper-parameters, gamma and nu, of the OCSVM based on the Gaussian Radial Basis Function Kernel were tested for each year. Following correlation analysis between the winter wheat area derived from the generated maps and the national statistical dataset at the city level, the map with the highest corresponding R2 was chosen as the AGWWS map for each year (0.77, 0.77, 0.80, 0.86, and 0.87 for 2000, 2005, 2010, 2015, and 2021, respectively). The AGWWS maps ultimately achieved an average Overall Accuracy of 81.65%. The study then explores the Non-Grain Production of Winter Wheat (NGPOWW) by analyzing winter wheat change maps from 2000–2005, 2005–2010, 2005–2010, and 2015–2021 in the HHH Plain. Despite an overall increase in the total planted area of winter wheat, the NGPOWW phenomena has led to concerning winter wheat planting marginalization. Compensatory winter wheat areas are notably situated in mountainous and suburban cultivated lands with low qualities. Consequently, despite the apparent expansion in planted areas, winter wheat production is anticipated to be adversely affected. The findings highlight the necessity for improved cultivated land protection policies monitoring the land quality of the compensation and setting strict quota limits on occupations.

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

The goal of increasing efficiency and durability of fuel cells can be achieved through optimal control of their operating conditions. In order to implement such controllers, accurate and computationally efficient fuel cell models must be developed. This work presents a hybrid (physics-based and data-driven), control-oriented model for approximating the output voltage of proton exchange membrane fuel cells (PEMFCs) while operating under dynamical conditions. First, a physics-based model, built from simplified electrochemical, membrane dynamics and mass conservation equations, is developed and validated through experimental data. Second, a data-driven, neural network (echo state network) is trained, fitted and tested with the same dataset. Then, the hybrid model is formed as a parallel structure, where the simplified physics-based model and the trained data-driven model are merged through an algorithm based on Gaussian radial basis functions. The merging algorithm compares the output of both single models and assigns weights for computing the prediction of the hybrid result. The proposed hybrid model structure is successfully trained, validated and tested with an experimental dataset originating from fuel cells within an automotive PEMFC stack. The hybrid model is assessed through the mean square error index, with the result of a low tracking error.

Implementation of a non-linear SVM classification for seizure EEG signal analysis on FPGA

An automatic detection method for disease diagnosis plays an important role in the medical field. A computer-aided diagnosis (CAD) system for seizure detection using electroencephalogram (EEG) data is significant in protecting patients' lives. This paper presents machine learning algorithm-based seizure detection approach implemented in both software and hardware using EEG recordings. The input EEG signal is subjected to pre-processing, decomposition using wavelet transform and classification by a support vector machine (SVM) classifier. The maximum and variance statistical values of the decomposed EEG sub-bands are computed to generate a feature vector, while classification accuracy and computational complexity are taken into account. Gaussian Radial Basis Function (RBF) kernel based Non-Linear SVM (NLSVM) is used in this proposed approach to solve both two-class and three-class classification problems. In three-class classification, the one-against-one (OAO) technique is used. The competency of the proposed technique is evaluated using two distinct datasets provided by Bonn University, Germany & Neurology and Sleep Centre, New Delhi. The adequate parameters of the NLSVM classifier model are determined by using MATLAB software in the training phase and the best performing NLSVM model is selected for implementation on the Field-Programmable Gate Array (FPGA). The proposed seizure detection method is implemented with Verilog code and examined on a Xilinx Zynq-7000 XC7Z020 FPGA board to evaluate its performance. The experimental results show that the proposed method with a minimal number of features achieved a good performance metric for seizure detection and is comparable with that of the state-of-the-art methods.