- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Prediction of two-dimensional topography of laser cladding based on neural network
摘要： The two-dimensional morphology of the cladding layer has an important influence on the quality of the cladding layer and the crack tendency. Using the powerful nonlinear processing ability of the single hidden layer feedforward neural network, a prediction model between the cladding technological parameters and the two-dimensional morphology of the cladding layer is established. Taking the cladding parameters as the input and the two-dimensional morphology of the cladding as the output, the experimental data is used to train the network to achieve a high-level mapping of the input and output. On this basis, the algorithm of extreme learning machine is used to optimize the single hidden layer feedforward neural network to overcome the problems of slow convergence speed, more network training parameters and easy local convergence in back-propagation algorithm. The results show that the relationship between the cladding process parameters and the two-dimensional morphology of the cladding layer can be roughly reflected by the back-propagation algorithm. However, the prediction results are not stable and the error rate is between 10% and 40%. The neural network optimized by the extreme learning machine is utilized to get a better prediction result. The error rate is 10–20%.
关键词： extreme learning machine.,BP neural network,Layer cladding,morphology prediction
更新于2025-11-28 14:24:20
-
Nondestructive Detection of Postharvest Quality of Cherry Tomatoes Using a Portable NIR Spectrometer and Chemometric Algorithms
摘要： The aim of this study was to assess the applicability of a portable NIR spectroscopy system and chemometric algorithms in intelligently detecting postharvest quality of cherry tomatoes. The postharvest quality of cherry tomatoes was evaluated in terms of firmness, soluble solids content (SSC), and pH, and a portable NIR spectrometer (950–1650 nm) was used to obtain the spectra of cherry tomatoes. Partial least square (PLS), support vector machine (SVM), and extreme learning machine (ELM) were applied to predict the postharvest quality of cherry tomatoes from their spectra. The effects of different preprocessing techniques, including Savitzky–Golay (S-G), multiplicative scattering correction (MSC), and standard normal variate (SNV) on prediction performance were also evaluated. Firmness, SSC and pH values of cherry tomatoes decreased during storage period, based on which the tomato samples could be classified into two distinct clusters. Similarly, cherry tomatoes with different storage time could also be separated by the NIR spectroscopic characteristics. The best prediction accuracy was obtained from ELM algorithms using the raw spectra with Rp2, RMSEP, and RPD values of 0.9666, 0.3141 N, and 5.6118 for firmness; 0.9179, 0.1485%, and 3.6249 for SSC; and 0.8519, 0.0164, and 2.7407 for pH, respectively. Excellent predictions for firmness and SSC (RPD value greater than 3.0), good prediction for pH (RPD value between 2.5 and 3.0) were obtained using ELM model. NIR spectroscopy is capable of intelligently detecting postharvest quality of cherry tomatoes during storage.
关键词： Partial least square,Extreme learning machine,Support vector machine,Cherry tomato,Near infrared spectroscopy
更新于2025-09-23 15:23:52
-
[IEEE 2018 3rd International Conference on Computer Science and Engineering (UBMK) - Sarajevo, Bosnia and Herzegovina (2018.9.20-2018.9.23)] 2018 3rd International Conference on Computer Science and Engineering (UBMK) - Hyperspectral Image Classification Using Reduced Extreme Learning Machine
摘要： In the classification of hyperspectral images, kernel based approaches have been shown to be successful results. Too much training or testing data in the images increases the computation time and memory requirements in the kernel computations. Extreme learning machines that can be used with the kernel approach also need the same requirements in kernel computations. In this study, improvements were made in terms of computation time and memory using reduced kernel extreme learning machines (RKELM). The obtained results are presented comparatively through the tables of performance and time information with kernel extreme learning machine (KELM).
关键词： classification,spectral information,Hyperspectral images,reduced kernel extreme learning machine
更新于2025-09-23 15:23:52
-
Blind Noisy Image Quality Assessment Using Sub-Band Kurtosis
摘要： Noise that afflicts natural images, regardless of the source, generally disturbs the perception of image quality by introducing a high-frequency random element that, when severe, can mask image content. Except at very low levels, where it may play a purpose, it is annoying. There exist significant statistical differences between distortion-free natural images and noisy images that become evident upon comparing the empirical probability distribution histograms of their discrete wavelet transform (DWT) coefficients. The DWT coefficients of low- or no-noise natural images have leptokurtic, peaky distributions with heavy tails; while noisy images tend to be platykurtic with less peaky distributions and shallower tails. The sample kurtosis is a natural measure of the peakedness and tail weight of the distributions of random variables. Here, we study the efficacy of the sample kurtosis of image wavelet coefficients as a feature driving an extreme learning machine which learns to map kurtosis values into perceptual quality scores. The model is trained and tested on five types of noisy images, including additive white Gaussian noise, additive Gaussian color noise, impulse noise, masked noise, and high-frequency noise from the LIVE, CSIQ, TID2008, and TID2013 image quality databases. The experimental results show that the trained model has better quality evaluation performance on noisy images than existing blind noise assessment models, while also outperforming general-purpose blind and full-reference image quality assessment methods.
关键词： sub-band,discrete wavelet transform (DWT),extreme learning machine (ELM),kurtosis,Blind noisy image quality assessment
更新于2025-09-23 15:23:52
-
An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images
摘要： Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang’s method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used.
关键词： auto-encoder,regularized extreme learning machine,security,HOG-SGF feature extraction,visible and multispectral palmprint images
更新于2025-09-23 15:22:29
-
Deep convolutional representations and kernel extreme learning machines for image classification
摘要： Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image classification and related tasks. However, the fully-connected layers in CNN are not robust enough to serve as a classifier to discriminate deep convolutional features, due to the local minima problem of back-propagation. Kernel Extreme Learning Machines (KELMs), known as an outstanding classifier, can not only converge extremely fast but also ensure an outstanding generalization performance. In this paper, we propose a novel image classification framework, in which CNN and KELM are well integrated. In our work, Densely connected network (DenseNet) is employed as the feature extractor, while a radial basis function kernel ELM instead of linear fully connected layer is adopted as a classifier to discriminate categories of extracted features to promote the image classification performance. Experiments conducted on four publicly available datasets demonstrate the promising performance of the proposed framework against the state-of-the-art methods.
关键词： Extreme learning machine,Neural network,Image classification
更新于2025-09-23 15:21:21
-
Suspended Sediment Concentration Estimation from Landsat Imagery along the Lower Missouri and Middle Mississippi Rivers Using an Extreme Learning Machine
摘要： Monitoring and quantifying suspended sediment concentration (SSC) along major fluvial systems such as the Missouri and Mississippi Rivers provide crucial information for biological processes, hydraulic infrastructure, and navigation. Traditional monitoring based on in situ measurements lack the spatial coverage necessary for detailed analysis. This study developed a method for quantifying SSC based on Landsat imagery and corresponding SSC data obtained from United States Geological Survey monitoring stations from 1982 to present. The presented methodology first uses feature fusion based on canonical correlation analysis to extract pertinent spectral information, and then trains a predictive reflectance–SSC model using a feed-forward neural network (FFNN), a cascade forward neural network (CFNN), and an extreme learning machine (ELM). The trained models are then used to predict SSC along the Missouri–Mississippi River system. Results demonstrated that the ELM-based technique generated R2 > 0.9 for Landsat 4–5, Landsat 7, and Landsat 8 sensors and accurately predicted both relatively high and low SSC displaying little to no overfitting. The ELM model was then applied to Landsat images producing quantitative SSC maps. This study demonstrates the benefit of ELM over traditional modeling methods for the prediction of SSC based on satellite data and its potential to improve sediment transport and monitoring along large fluvial systems.
关键词： suspended sediment,Landsat,water quality,extreme learning machine,machine learning
更新于2025-09-23 15:21:01
-
Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm
摘要： Hyperspectral image classi?cation is a hot issue in the ?eld of remote sensing. It is possible to achieve high accuracy and strong generalization through a good classi?cation method that is used to process image data. In this paper, an ef?cient hyperspectral image classi?cation method based on improved Rotation Forest (ROF) is proposed. It is named ROF-KELM. Firstly, Non-negative matrix factorization( NMF) is used to do feature segmentation in order to get more effective data. Secondly, kernel extreme learning machine (KELM) is chosen as base classi?er to improve the classi?cation ef?ciency. The proposed method inherits the advantages of KELM and has an analytic solution to directly implement the multiclass classi?cation. Then, Q-statistic is used to select base classi?ers. Finally, the results are obtained by using the voting method. Three simulation examples, classi?cation of AVIRIS image, ROSIS image and the UCI public data sets respectively, are conducted to demonstrate the effectiveness of the proposed method.
关键词： extreme learning machine,rotation forest,hyperspectral image classi?cation,Q-statistic
更新于2025-09-23 15:21:01
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Evaluation of Different Regularization Methods for the Extreme Learning Machine Applied to Hyperspectral Images
摘要： During recent years, many regularization techniques have been proposed to deal with ill-posed problems related to hyperspectral image classification, in which the limited number of training samples contrasts with the very high spectral dimensionality. However, the intrinsic structure of a hyperspectral image often depends on the specific scene and spectrometer, although regularizers like Ridge, LASSO, etc, have been widely used in practical applications. Instead of imposing these regularizers to the probabilistic output of a classifier, this work evaluates the use of extreme learning machines (ELM) with output weights of a single-hidden layer feed-forward neural network (SLFN) regularized with Ridge and LASSO priors, respectively. Experimental results with several real hyperspectral images are conducted to compare the performance and adaptation of these two regularizers with the original ELM in classification scenarios.
关键词： Ridge,LASSO,regularization,hyperspectral image classification,extreme learning machine
更新于2025-09-23 15:21:01
-
[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Laser Stimulation of Retina and Optic Nerve in Children with Anisometropic Amblyopia
摘要： Extreme learning machine (ELM) is emerged as an effective, fast, and simple solution for real-valued classification problems. Various variants of ELM were recently proposed to enhance the performance of ELM. Circular complex-valued extreme learning machine (CC-ELM), a variant of ELM, exploits the capabilities of complex-valued neuron to achieve better performance. Another variant of ELM, weighted ELM (WELM) handles the class imbalance problem by minimizing a weighted least squares error along with regularization. In this paper, a regularized weighted CC-ELM (RWCC-ELM) is proposed, which incorporates the strength of both CC-ELM and WELM. Proposed RWCC-ELM is evaluated using imbalanced data sets taken from Keel repository. RWCC-ELM outperforms CC-ELM and WELM for most of the evaluated data sets.
关键词： extreme learning machine,Real valued classification,complex valued neural network,class imbalance problem,regularization,weighted least squares error
更新于2025-09-23 15:19:57