- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Biometric iris recognition using radial basis function neural network
摘要： The consistent and efficient method for the identification of biometrics is the iris recognition in view of the fact that it has richness in texture information. A good number of features performed in the past are built on handcrafted features. The proposed method is based on the feed-forward architecture and uses k-means clustering algorithm for the iris patterns classification. In this paper, segmentation of iris is performed using the circular Hough transform that realizes the iris boundaries in the eye and isolates the region of iris with no eyelashes and other constrictions. Moreover, Daugman's rubber sheet model is used to transform the resultant iris portion into polar coordinates in the process of normalization. A unique iris code is generated by log-Gabor filter to extract the features. The classification is achieved using neural network structures, the feed-forward neural network and the radial basis function neural network. The experiments have been conducted using the Chinese Academy of Sciences Institute of Automation (CASIA) iris database. The proposed system decreases computation time, size of the database and increases the recognition accuracy as compared to the existing algorithms.
关键词： Feed-forward neural network (FNN),Iris segmentation,Normalization,Biometrics,Radial basis function neural network (RBFNN),Iris recognition
更新于2025-09-23 15:23:52
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Numerical Modeling of Acousto-Plasmonic Coupling in Metallic Nanoparticles
摘要： We describe a computational approach to study the acousto-plasmonic coupling in metallic nanoparticles. We use the high level multiphysics finite element software FreeFEM developed at Laboratoire Jacques-Louis Lions of Pierre and Marie Curie University (Paris). Our numerical method determines one after the other the acoustic modes of the nanoparticles and the modulation of the electromagnetic properties. The transfer of the deformed geometries between acoustic and electromagnetic simulations is realized by an update of the nodal coordinates situated at the boundary between the nanoparticle and its host medium, and using a mesh deformation algorithm based on radial basis function interpolation. Thus we theoretically investigate different coupling mechanisms between confined vibrations and surface plasmons: shape effect, electron density effect due to changes of the nanoparticle volume and inter-band transitions effect which is evaluated by the deformation potential mechanism.
关键词： acousto-plasmonic coupling,radial basis function interpolation,FreeFEM,finite element method,metallic nanoparticles
更新于2025-09-23 15:21:01
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[IEEE 2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) - Ghaziabad (2018.2.9-2018.2.10)] 2018 4th International Conference on Computational Intelligence & Communication Technology (CICT) - Development of a Decision-Based Neural Network for a Day-Ahead Prediction of Solar PV Plant Power Output
摘要： Day-ahead photovoltaic power prediction is vital for policy making and providing necessary backup capacities. Previous researchers include the implementation of time series, auto-regression and Soft computing techniques like Artificial Neural Networks and Fuzzy Logic. Artificial Neural Networks provides a better fit to complex, non-linear and error-prone data. The paper shows a comparative study of a Radial Basis Neural Network Schema (exact fit), a ‘k-means’ Radial Neural Network, and a Feed Forward Neural Network with Levenberg-Marquardt error backpropagation designed for the prediction of power output at an hourly resolution. The ability of the Neural Network to be trained to adapt to a previous set of data and then interpolate or extrapolate to the new data set has been exploited. The proposed model uses five meteorological variables and uses recorded data collected from the SN Mohanty PV Power Plant. Training of neural network is done on a monthly basis so that normalization constants of variables can be lower and better mapping can be produced. An improved decision-based schematic using Neural Networks is proposed which combines the advantages of both Radial Basis Function (exact fit) and FFNN.
关键词： solar photovoltaic power plant,Radial Basis,Artificial Neural Network,Decision-based,ANN
更新于2025-09-23 15:21:01
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[IEEE 2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD) - Winterton, South Africa (2019.8.5-2019.8.6)] 2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD) - Maximum Rooftop Photovoltaic Hosting Capacity with Harmonics as Limiting Factor a?? Case Study for Mauritius
摘要： This paper proposes an adaptive radial basis function (RBF) neural network (NN) fuzzy control scheme to enhance the performance of shunt active power ?lter (APF).The RBF NN is utilized on the approximation of nonlinear function in the APF dynamic model and the weights of the RBF NN are adjusted online according to adaptive law from the Lyapunov stability analysis to ensure the state hitting the sliding surface and sliding along it. In order to compensate the network approximation error and eliminate the existing chattering, the sliding mode control term is adjusted by adaptive fuzzy systems, which can enhance the robust performance of the system. The simulation results of APF using the proposed method con?rm the effectiveness of the proposed controller, demonstrating the outstanding compensation performance and strong robustness.
关键词： adaptive fuzzy control,Sliding mode control,radial basis function neural network (RBF NN)
更新于2025-09-23 15:19:57
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[IEEE 2018 11th UK-Europe-China Workshop on Millimeter Waves and Terahertz Technologies (UCMMT) - HangZhou, China (2018.9.5-2018.9.7)] 2018 11th UK-Europe-China Workshop on Millimeter Waves and Terahertz Technologies (UCMMT) - Graphene-based THz Antenna with A Graphene-metal CPW Feeding Structure
摘要： This paper proposes an adaptive radial basis function (RBF) neural network (NN) fuzzy control scheme to enhance the performance of shunt active power ?lter (APF).The RBF NN is utilized on the approximation of nonlinear function in the APF dynamic model and the weights of the RBF NN are adjusted online according to adaptive law from the Lyapunov stability analysis to ensure the state hitting the sliding surface and sliding along it. In order to compensate the network approximation error and eliminate the existing chattering, the sliding mode control term is adjusted by adaptive fuzzy systems, which can enhance the robust performance of the system. The simulation results of APF using the proposed method con?rm the effectiveness of the proposed controller, demonstrating the outstanding compensation performance and strong robustness.
关键词： adaptive fuzzy control,Sliding mode control,radial basis function neural network (RBF NN)
更新于2025-09-19 17:13:59
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A Novel Fault Classification Approach for Photovoltaic Systems
摘要： Photovoltaic (PV) energy has become one of the main sources of renewable energy and is currently the fastest-growing energy technology. As PV energy continues to grow in importance, the investigation of the faults and degradation of PV systems is crucial for better stability and performance of electrical systems. In this work, a fault classification algorithm is proposed to achieve accurate and early failure detection in PV systems. The analysis is carried out considering the feature extraction capabilities of the wavelet transform and classification attributes of radial basis function networks (RBFNs). In order to improve the performance of the proposed classifier, the dynamic fusion of kernels is performed. The performance of the proposed technique is tested on a 1 kW single-phase stand-alone PV system, which depicted a 100% training efficiency under 13 s and 97% testing efficiency under 0.2 s, which is better than the techniques in the literature. The obtained results indicate that the developed method can effectively detect faults with low misclassification.
关键词： feature extraction,radial basis function networks (RBFN),fault classification,photovoltaic system,wavelet analysis,kernels
更新于2025-09-19 17:13:59
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Mode Matching Analysis of Waveguide Components Exploiting the Variational Meshless Method
摘要： This letter presents the analysis of waveguide components comprising a cascade of step junctions by mode matching in conjunction with the variational meshless method (VMM). In particular, a large number of modal fields of the waveguides are calculated very efficiently by the VMM. Moreover, collocation points are randomly distributed in each waveguide cross section, and the coupling coefficients are obtained regardless of their mutual distribution. The presented technique is validated through the analysis of two junctions reported in the literature and by comparison with an finite element method (FEM) full-wave software.
关键词： radial basis functions (RBFs),waveguide mode,meshless method,Eigenproblem
更新于2025-09-11 14:15:04
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Line–planes deflectometry
摘要： The key issue in phase measurement deflectometry is the solution of incident light which is used to determine the surface normal. To restore the 3D mirror surface information, line–planes deflectometry is proposed to determine the surface normal from the reflected light and the incident light planes containing incident light. To this end, a point light source is used to replace the continuous surface light source of traditional deflectometry. Accordingly, the incident light is calculated by the intersection of incident light planes which are determined by two or more projected lights of a point light source. Finally, the precise mirror surface is restored through the radial basis function interpolation from the gradient which is transformed from the normal information. To verify the proposed method, a line–planes deflectometry system is developed with two projectors and one camera. The system can be applied for two measurement modes concerning different measurement requirements: the ‘line–plane model’ and the ‘line–planes model’. The ‘line–plane model’ can be inferred with a measurement error of less than 0.25 mm to measure objects with low accuracy and non-connectivity. In contrast, the ‘line–planes model’ can be used to measure objects with high accuracy and good area connectivity, and the measurement error is about 1 μm.
关键词： point light source,radial basis function interpolation,line–planes deflectometry,incident light plane,incident light
更新于2025-09-09 09:28:46
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Focus Assessment Method of Gaze Tracking Camera Based on ε-Support Vector Regression
摘要： In order to capture an eye image of high quality in a gaze-tracking camera, an auto-focusing mechanism is used, which requires accurate focus assessment. Although there has been previous research on focus assessment in the spatial or wavelet domains, there are few previous studies that combine all of the methods of spatial and wavelet domains. Since all of the previous focus assessments in the spatial or wavelet domain methods have disadvantages, such as being affected by illumination variation, etc., we propose a new focus assessment method by combining the spatial and wavelet domain methods for the gaze-tracking camera. This research is novel in the following three ways, in comparison with the previous methods. First, the proposed focus assessment method combines the advantages of spatial and wavelet domain methods by using ε-support vector regression (SVR) with a symmetrical Gaussian radial basis function (RBF) kernel. In order to prevent the focus score from being affected by a change in image brightness, both linear and nonlinear normalizations are adopted in the focus score calculation. Second, based on the camera optics, we mathematically prove the reason for the increase in the focus score in the case of daytime images or a brighter illuminator compared to nighttime images or a darker illuminator. Third, we propose a new criterion to compare the accuracies of the focus measurement methods. This criterion is based on the ratio of relative overlapping amount (standard deviation of focus score) between two adjacent positions along the Z-axis to the entire range of focus score variety between these two points. Experimental results showed that the proposed method outperforms other methods.
关键词： ε-support vector regression with a symmetrical Gaussian radial basis function kernel,auto-focusing,focus assessment,camera optics,gaze-tracking camera
更新于2025-09-09 09:28:46
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Hyperspectral inversion of heavy metal content in reclaimed soil from a mining wasteland based on different spectral transformation and modeling methods
摘要： Conventional methods for investigating heavy metal contamination in soil are time consuming and expensive. We explored reflectance spectroscopy as an alternative method for assessing heavy metals. Four spectral transformation methods, first-order differential (FDR), second-order differential (SDR), continuum removal (CR) and continuous wavelet transform (CWT), are used for the original spectral data. Spectral preprocessing effectively eliminated the noise and baseline drifting and also highlighted the locations of the spectral feature bands. Partial least squares regression (PLSR) and radial basis function neural network (RBF) were used to study the hyperspectral inversion of four heavy metals (Cr, As, Ni, Cd). The inversion models of four heavy metals were established in the bands with the highest correlation coefficient. The inversion effects were evaluated by the coefficient of determination (R2), root mean square error (RMSE) and residual predictive deviation (RPD) indexes. The R values of the correlation coefficient were significantly improved after smoothing and spectral transformation compared to the original waveband. The method combining continuous wavelet transform (CWT) with radial basis function neural network (RBF) had the best inversion effect on the four heavy metals. When compared to partial least squares regression (PLSR), the RMSE values were reduced by approximately 2. The CWT-RBF method can be used as a means of inversion of heavy metals in mining wasteland reclaimed land.
关键词： Continuous Wavelet Transform,Heavy metal,Spectral analysis,Radial Basis Function Neural Network,Reclamation soil
更新于2025-09-04 15:30:14