- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Phase-Compensated Optical Fiber-Based Ultrawideband Channel Sounder
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
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - An Economic and Environmental Assessment of Residential Rooftop Photovoltaics with Second Life Batteries in the US
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2025-09-19 17:13:59
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[IEEE 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - Chengdu, China (2019.5.21-2019.5.24)] 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - Research on Predicting the Short-term Output of Photovoltaic (PV) Based on Extreme Learning Machine Model and Improved Similar Day
摘要： In this paper, an extreme learning machine model based on the improved similar day to predict the short-term output of photovoltaic is presented. The Pearson correlation coefficient is used to analyze the influence of various meteorological factors on the output of photovoltaic power generation, so as to find out the meteorological factors that have a greater impact on photovoltaic output. And then the prediction model is to be established with combining the fuzzy clustering method by improving the similar day selection method. The multi-day photovoltaic output data with the highest correlation about the day to be tested is used to train the extreme learning machine neural network which is then used to predict the PV output of the day to be measured. Finally, the experimental results show that the proposed method has higher prediction accuracy and shorter calculation time than the traditional prediction method, and what's more, the algorithm is simple and the prediction cost is low. It has a wide application value and research space.
关键词： extreme learning machine,similar day,PV output prediction
更新于2025-09-16 10:30:52
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Extreme learning machine-based receiver for MIMO LED communications
摘要： This work concerns receiver design for light-emitting diode (LED) multiple input multiple output (MIMO) communications where the LED nonlinearity can severely degrade the performance of communications. We firstly propose an extreme learning machine (ELM) based receiver to jointly handle the LED nonlinearity and cross-LED interference. Then, by taking advantage of the features of the ELM, we propose to use a circulant structure for the input weight matrix and the fast Fourier transform (FFT) for implementation, leading to significant computational complexity reduction. It is demonstrated that, the proposed ELM based receivers can handle the nonlinearity and interference much more effectively compared to conventional techniques, and the low complexity ELM-based receiver with circulant input matrix delivers almost the same performance as the receiver based on the conventional ELM.
关键词： Extreme learning machine,Nonlinearity,Post-distortion,Feedforward neural networks,LED communications
更新于2025-09-16 10:30:52
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Prediction short-term photovoltaic power using improved chicken swarm optimizer - Extreme learning machine model
摘要： Photovoltaic power generation is greatly affected by weather conditions while the photovoltaic power has a certain negative impact on the power grid. The power sector takes certain measures to abandon photovoltaic power generation, thus limiting the development of clean energy power generation. This study is to propose an accurate short-term photovoltaic power prediction method. A new short-term photovoltaic power output prediction model is proposed Based on extreme learning machine and intelligent optimizer. Firstly, the input of the model is determined by correlation coef?cient method. Then the chicken swarm optimizer is improved to strengthen the convergence. Secondly, the improved chicken swarm optimizer is used to optimize the weights and the extreme learning machine thresholds to improve the prediction effect. Finally, the improved chicken swarm optimizer extreme learning machine model is used to predict the photovoltaic power under different weather conditions. The testing results show that the average mean absolute percentage error and root mean square error of improved chicken swarm optimizer - extreme learning machine model are 5.54% and 3.08%. The proposed method is of great signi?cance for the economic dispatch of power systems and the development of clean energy.
关键词： Extreme learning machine,Model-driven method,Photovoltaic power generation,Intelligent optimizer,Power prediction
更新于2025-09-16 10:30:52
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Data-Driven Robust Coordination of Generation and Demand-side in Photovoltaic Integrated All-Electric Ship Microgrids
摘要： Fully electrified ships, which is known as the “all-electric ships (AESs)”, have the potentials to bring great economic /environmental benefits. To further improve the energy efficiency of AESs, PV generations are gradually integrated, which introduces uncertainties to the AES operation. However, current researches mostly focus on sizing problem whereas rarely concern the operation. In this perspective, a data-driven robust coordination of generation and demand-side is proposed to properly address the onboard PV generation uncertainties as well as reducing the fuel cost of AESs, which consists of an extreme learning machine (ELM) based PV uncertainty forecasting method and a two-stage operating framework, where the first stage for the worst PV generation case and the second stage targets at the uncertainty realization. A 4-DG AES is implemented into the case study and the simulation results show that the ELM-based method can well characterize the PV uncertainties, and the two-stage operating framework can well accommodate the onboard PV uncertainties. Further analysis also demonstrates the proposed method has enough flexibility when facing working condition variations.
关键词： mobile microgrid,robustness,extreme learning machine,coordination of generation and demand-side,All-electric ship,photovoltaic generation
更新于2025-09-12 10:27:22
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Temperature extraction for Brillouin optical fiber sensing system based on extreme learning machine
摘要： The use of extreme learning machine (ELM) network to extract temperature distribution from the measured Brillouin gain spectra (BGSs) along the sensing fiber obtained by Brillouin optical fiber sensors is proposed and demonstrated experimentally. Compared with conventional curve fitting method (CFM), ELM network trained by a set of ideal BGSs can extract temperature information directly from the measured BGSs obtained by Brillouin optical time domain reflectometer (BOTDR) system without the need of determining Brillouin frequency shift (BFS) and converting BFS to temperature. The BGSs linewidth is taken into account to construct the ideal BGSs by using Pseudo-Voigt curve for ELM training. The performance of ELM is analyzed in detail and compared with that of widely-used Lorentzian CFM, and the experiment results show that ELM can provide higher accuracy even at large frequency scanning step and faster processing speed. Therefore, the proposed ELM approach is feasible and effective for temperature extraction in Brillouin optical fiber sensing system.
关键词： Nonlinear optical signal processing,Brillouin scattering,Temperature extraction,Optical fiber sensors,Extreme learning machine
更新于2025-09-12 10:27:22
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[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 - Stacked Autoencoders for Multiclass Change Detection in Hyperspectral Images
摘要： Change detection (CD) in multitemporal datasets is a key task in remote sensing. In this paper, a scheme to perform multi-class CD for remote sensing hyperspectral datasets extracting features by means of Stacked Autoencoders (SAEs) is introduced. The scheme combines multiclass and binary CD to obtain an accurate multiclass change map. The multiclass CD begins with the fusion of the multitemporal data followed by Feature Extraction (FE) by SAEs. The binary CD is based on the spectral information by calculating pixel-wise distances and thresholding, and it also incorporates spatial information through watershed segmentation. The processed image is filtered by using the binary CD map and later classified by a Support Vector Machine or an Extreme Learning Machine algorithm. The scheme was evaluated over a multitemporal hyperspectral dataset obtained from the Hyperion sensor. Experimental results show the effectiveness of the proposed scheme using a SAE for extracting the relevant features of the fused information when compared to other published FE methods.
关键词： Change Detection,Stacked Autoencoder,Feature Extraction,Hyperspectral,Support Vector Machine,Extreme Learning Machine
更新于2025-09-10 09:29:36
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Fast Active Learning for Hyperspectral Image Classification using Extreme Learning Machine
摘要： Due to undulating and complexity of the earth’s surface, obtaining the training samples for remote sensing data is time consuming and expensive. Therefore, it is highly desirable to design a model that uses as few labelled samples as possible and reducing the computational time. Several active learning (AL) algorithms have been proposed in the literature for the classification of hyperspectral images (HSI).However, its performance in term of computational time has not been focused yet. In this paper, we have proposed AL approach based on Extreme Learning Machine (ELM) that effectively decreases the computational time while maintaining the classification accuracy. Further, the effectiveness of the proposed approach has been depicted by comparing its performance with state-of-the-art AL algorithms in terms of classification accuracy and computational time as well. The ELM based active learning (ELM-AL) with different query strategies were conducted on two HSI data sets. The proposed approach achieves the classification accuracy up to 90% which is comparable to support vector machine (SVM) based AL (SVM-AL) approach but effectively reduces the computational time significantly by 1000 times. Thus proposed system shows the encouraging results with adequate classification accuracy while reducing the computation time drastically.
关键词： Uncertainty sampling,Remote Sensing Image,Extreme learning machine,Classification,Active learning,Uncertainty measure,Hyperspectral Image
更新于2025-09-09 09:28:46
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Segmentation and Classification of Optic Disc in Retinal Images
摘要： Image segmentation plays a vital role in image analysis for diagnosis of various retinopathy diseases. For the detection of glaucoma and diabetic retinopathy, manual examination of the optic disc is the standard clinical procedure. The proposed method makes use of the circular transform to automatically locate and extract the Optic Disc (OD) from the retinal fundus images. The circular transform operates with radial line operator which uses the multiple radial line segments on every pixel of the image. The maximum variation pixels along each radial line segments are taken to detect and segment OD. The input retinal images are preprocessed before applying circular transform. The optic disc diameter and the distance from optic disc to macula are found for a sample of 20 images. An Extreme Learning Machine classifier is used to train the neural network to classify the images as normal or abnormal. Its performance is compared with Support Vector Machine in terms of computation time and accuracy. It is found that computation time is less than 0.1 sec and accuracy is 97.14% for Extreme Learning Machine classifier.
关键词： extreme learning machine,Circular transform,optic disc,segmentation,macula
更新于2025-09-09 09:28:46