- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 12th International Conference on Communications (COMM) - Bucharest (2018.6.14-2018.6.16)] 2018 International Conference on Communications (COMM) - Supervized Change Detection for SAR Imagery Based on Processing of a Low Size Training Data Set by an Ensemble of Self-Organizing Maps
摘要： This paper presents a new method to improve accuracy of supervised change detection in Synthetic Aperture Radar (SAR) imagery. The model is based on the idea to apply a low size labeled dataset to the input of an Ensemble of Self-Organizing Maps (ESOM) for training data generation (TDG). The resulted synthetic data set produced by ESOM substitutes the initial authentically labeled sample set and it is used to train a supervised change detection classifier. The proposed method is evaluated using a TerraSAR-X image of 400x400 pixels acquired in the Fukushima region, Japan, before and after tsunami. As change detection classifiers we have comparatively considered Support Vector Machine (SVM), Nearest Neighbor (NN), the three-Nearest Neighbors (3-NN), and Likelihood Bayes classifier. The experimental results have confirmed the effectiveness of the proposed approach using only 100 authentic labeled pixels.
关键词： SAR images,ensemble of self-organizing maps (ESOM),virtual training data generation(VTDG),change detection
更新于2025-09-23 15:23:52
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Semisupervised Scene Classification for Remote Sensing Images: A Method Based on Convolutional Neural Networks and Ensemble Learning
摘要： The scarcity of labeled samples has been the main obstacle to the development of scene classification for remote sensing images. To alleviate this problem, the efforts have been dedicated to semisupervised classification which exploits both labeled and unlabeled samples for training classifiers. In this letter, we propose a novel semisupervised method that utilizes the effective residual convolutional neural network (ResNet) to extract preliminary image features. Moreover, the strategy of ensemble learning (EL) is adopted to establish discriminative image representations by exploring the intrinsic information of all available data. Finally, supervised learning is performed for scene classification. To verify the effectiveness of the proposed method, it is further compared with several state-of-the-art feature representation and semisupervised classification approaches. The experimental results show that by combining ResNet features with EL, the proposed method can obtain more effective image representations and achieve superior results.
关键词： remote sensing (RS) images,Semi-supervised classification,ensemble learning (EL),scene classification,Convolutional neural networks (CNNs)
更新于2025-09-23 15:23:52
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High-sensitivity three-axis vector magnetometry using the electron spin ensembles in single diamond
摘要： We demonstrate a three-axis vector magnetometer based on ensembles of negatively charged nitrogen vacancy centers in single-crystal diamond. Diamond with C3v symmetry was used to establish the coordinate system for vector magnetic field sensing. We control the external static magnetic field with three-axis Helmholtz coils. Four pairs of magnetic resonance peaks were obtained, which were used to calculate the three Cartesian components of the magnetic field with sensitivity of ~5 nT/√Hz for each Cartesian component, free of interaxis error. The magnetometer is suitable for single-chip manufacturing.
关键词： Nitrogen-Vacancy (NV) centers,ensemble,diamond,Magnetometry
更新于2025-09-23 15:22:29
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Hyperspectral Face Recognition with Patch-Based Low Rank Tensor Decomposition and PFFT Algorithm
摘要： Hyperspectral imaging technology with sufficiently discriminative spectral and spatial information brings new opportunities for robust facial image recognition. However, hyperspectral imaging poses several challenges including a low signal-to-noise ratio (SNR), intra-person misalignment of wavelength bands, and a high data dimensionality. Many studies have proven that both global and local facial features play an important role in face recognition. This research proposed a novel local features extraction algorithm for hyperspectral facial images using local patch based low-rank tensor decomposition that also preserves the neighborhood relationship and spectral dimension information. Additionally, global contour features were extracted using the polar discrete fast Fourier transform (PFFT) algorithm, which addresses many challenges relevant to human face recognition such as illumination, expression, asymmetrical (orientation), and aging changes. Furthermore, an ensemble classifier was developed by combining the obtained local and global features. The proposed method was evaluated by using the Poly-U Database and was compared with other existing hyperspectral face recognition algorithms. The illustrative numerical results demonstrate that the proposed algorithm is competitive with the best CRC_RLS and PLS methods.
关键词： spectral and spatial information,polar discrete fast Fourier transform,band fusion,ensemble classifier,global and local features,tensor decomposition,hyperspectral images
更新于2025-09-23 15:22:29
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Multi-scale sifting for mammographic mass detection and segmentation
摘要： Breast mass detection and segmentation are challenging tasks due to the fact that breast masses vary in size and appearance. In this work, we present a simultaneous detection and segmentation scheme for mammographic lesions that is constructed in a sifting architecture. It utilizes a novel region candidate selection approach and cascaded learning techniques to achieve state-of-the-art results while handling a high class imbalance. The region candidates are generated by a novel multi-scale morphological sifting (MMS) approach, where oriented linear structuring elements are used to sieve out the mass-like objects in mammograms including stellate patterns. This method can accurately segment masses of various shapes and sizes from the background tissue. To tackle the class imbalance problem, two different ensemble learning methods are utilized: a novel self-grown cascaded random forests (CasRFs) and the random under-sampling boost (RUSBoost). The CasRFs is designed to handle class imbalance adaptively using a probability-ranking based under-sampling approach, while RUSBoost uses a random under-sampling technique. This work is evaluated on two publicly available datasets: INbreast and DDSM BCRP. On INbreast, the proposed method achieves an average sensitivity of 0.90 with 0.9 false positives per image (FPI) using CasRFs and with 1.2 FPI using RUSBoost. On DDSM BCRP, the method yields a sensitivity of 0.81 with 3.1 FPI using CasRFs and with 2.9 FPI using RUSboost. The performance of the proposed method compares favorably to the state-of-the-art methods on both datasets, especially on highly spiculated lesions.
关键词： Morphological sifting,Mammography,Breast mass detection and segmentation,Cascaded random forest,Ensemble learning
更新于2025-09-23 15:22:29
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Modeling of circular fractal antenna using BFO-PSO-based selective ANN ensemble
摘要： Accurate design of miniaturized antenna is constrained by the limited well‐formulated exact mathematical expressions. Demands for smart devices with features like portability, implantability, and configurability have further placed bigger challenges in front of the antenna design engineers or scientists. As a part of the search for various solutions, many innovative approaches have been proposed by various authors in different literatures. Application of soft computing is also another design approach to accurate design of fractal antenna. Here, the authors have attempted to propose a better solution to miniaturized antenna and its design. A fractal antenna based on circular outer geometry has been proposed as a solution to the search of miniaturized antennas, and a particle swarm optimization–based selective artificial neural networks ensemble is developed, which is employed as the objective function of a bacterial foraging optimization algorithm leading to a hybridized algorithm. The developed hybrid algorithm is utilized to develop the proposed antenna at 2.45 GHz. A good agreement of the simulated, desired, and experimental results validates the proposed design approach.
关键词： BFO,ANN ensemble,ISM band,fractal antenna,PSO
更新于2025-09-23 15:22:29
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An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting
摘要： As the world is aware, the trend of generating energy sources has been changing from conventional fossil fuels to sustainable energy. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased, and solar and wind power, typically, are driving this energy change. However, renewable energy sources highly depend on weather conditions and have intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and accurate prediction of renewable energy output is essential to address this. To solve this issue, much research has studied prediction models, and machine learning is one of the typical methods. In this paper, we used a bagging model to predict solar energy output. Bagging generally uses a decision tree as a base learner. However, to improve forecasting accuracy, we proposed a bagging model using an ensemble model as a base learner and adding past output data as new features. We set base learners as ensemble models, such as random forest, XGBoost, and LightGBMs. Also, we used past output data as new features. Results showed that the ensemble learner-based bagging model using past data features performed more accurately than the bagging model using a single model learner with default features.
关键词： ensemble,decision tree,bagging,Light GBM,lagged data,machine learning,random forest,XGBoost,photovoltaic power forecasting
更新于2025-09-23 15:21:01
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[IEEE 2019 29th Australasian Universities Power Engineering Conference (AUPEC) - Nadi, Fiji (2019.11.26-2019.11.29)] 2019 29th Australasian Universities Power Engineering Conference (AUPEC) - Adaptive Boosting and Bootstrapped Aggregation based Ensemble Machine Learning Methods for Photovoltaic Systems Output Current Prediction
摘要： Photovoltaics output current prediction received great deal of attention in recent years, due to the high penetration level of PV utilization. The intermittent nature of PV systems, in addition to the fast-varying irradiance levels, provoked the need for fast, accurate and reliable forecasting techniques. Machine Learning (ML) methods have been proven to effectively solve regression-based prediction problems. ML methods that utilize multiple models to construct decision trees are called Ensemble Machine Learning (EML) algorithms. This paper presents a comparison study of two EML methods namely; AdaBoost and Random Forest for photovoltaics application. A dataset of fast varying environmental conditions has been employed and the terminal current of the experimental setup has been augmented based on the mathematical model and the use of an evolutionary algorithm. The mathematical model has been examined for several irradiance and temperature levels and adjusted based on the manufacturer datasheet. Random Forest overall absolute error distribution had the lowest mean and standard deviation. Results shows the superior performance of Random Forest over AdaBoost in terms of absolute error, on the contrary, AdaBoost absolute error distribution is scattered with larger quartiles limits. Random Forest overall absolute error distribution had the lowest mean of 0.27% with a standard deviation of 0.91%, however, AdaBoost absolute error mean was as high as 34.5% with a standard deviation of 15.8% relative to the mathematical model. Accurate predictions can be integrated in an EML based maximum power point tracking (MPPT) scheme.
关键词： ensemble machine learning,adaptive boosting,photovoltaics,regression decision trees,single diode model
更新于2025-09-23 15:21:01
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A general approach to evaluate the ensemble cross-correlation response for PIV using Kernel density estimation
摘要： Cross-correlation in particle image velocimetry is well known to behave as a non-linear operator, depending heavily on the distribution of tracer images and image quality. While analytical descriptors of the correlation response have so far been dealt with for simplistic flow cases, in this work a methodology is presented based on Kernel density estimation to retrieve the inherent correlation response to any deterministic flow field. The new approach bypasses the need for Monte-Carlo simulations and its inherent sensitivity to parameter settings make it a more efficient alternative to analyse filtering of the underlying velocity field due to image cross-correlation. The derivation of the underlying equations is presented and a numerical assessment corroborates the suitability of the approach to mimic ensemble correlation.
关键词： Ensemble correlation,Particle image velocimetry,Flow field filtering,Kernel density estimation,Cross-correlation
更新于2025-09-23 15:21:01
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Continuous-variable entanglement distillation between remote quantum nodes
摘要： The development of quantum network relies on high-quality entanglement between remote quantum nodes. In reality the unavoidable decoherence limits the quality of entangled quantum nodes, however, entanglement distillation can overcome this problem. Here we propose an experimentally feasible scheme of continuous-variable entanglement generation, storage, and distillation between distant quantum nodes, which only requires the atomic ensemble quantum memory and balanced homodyne detection (BHD). Initially one copy of a bipartite entangled state of light, suffering phase fluctuations during the distributions, is stored in two distant atomic ensembles so that the atomic ensembles are entangled. Within the storage lifetime, by distributing and storing another copy of entangled optical modes in these two atomic ensembles, the distillation on entangled atomic spin waves can be implemented based on the post-selection of the BHD result. Our scheme provides a highly entangled state between remote quantum nodes for downstream applications, and enables the extension to multipartite entanglement distillation in a large-scale quantum network.
关键词： balanced homodyne detection,continuous-variable,entanglement distillation,quantum network,atomic ensemble
更新于2025-09-23 15:21:01