- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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HCKBoost: Hybridized composite kernel boosting with extreme learning machines for hyperspectral image classification
摘要： Utilization of contextual information on the hyperspectral image (HSI) analysis is an important fact. On the other hand, multiple kernels (MKs) and hybrid kernels (HKs) in connection with kernel methods have significant impact on the classification process. Activation of spatial information via composite kernels (CKs) and exploiting hidden features of the spectral information via MKs and HKs have been shown great successes on hyperspectral images separately. In this work, it is aimed to aggregate composite and hybrid kernels to obtain high classification success with a boosting based community learner. Spatial and spectral hybrid kernels are constructed using weighted convex combination approach with respect to individual success of the predefined kernels. Composite kernel formation is realized with certain proportions of the obtained spatial and spectral HKs. Computationally fast and effective extreme learning machine (ELM) classification algorithm is adopted. Since, main objective is to obtain optimal kernel during ensemble formation operation, unlike the standard MKL methods, proposed method disposes off the complex optimization processes and allows multi-class classification. Pavia University, Indian Pines, and Salinas hyperspectral scenes that have ground truth information are used for simulations. Hybridized composite kernels (HCK) are constructed using Gaussian, polynomial, and logarithmic kernel functions with various parameters and then obtained results are presented comparatively along with the state-of-the-art MKL, CK, sparse representation, and single kernel based methods.
关键词： Hyperspectral images,Composite kernels,Adaptive boosting,Extreme learning machines,Hybrid kernels
更新于2025-09-23 15:22:29
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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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Machine learning based temperature prediction of poly( <i>N</i> -isopropylacrylamide)-capped plasmonic nanoparticle solutions
摘要： The temperature-dependent optical properties of gold nanoparticles that are capped with the thermo-sensitive polymer: ‘poly(N-isopropylacrylamide)’ (PNIPAM), have been studied extensively for several years. Also, their suitability to function as nanoscopic thermometers for bio-sensing applications has been suggested numerous times. In an attempt to establish this, many have studied the temperature-dependent optical resonance characteristics of these particles; however, developing a simple mathematical relationship between the optical measurements and the solution temperature remains an open challenge. In this paper, we attempt to systematically address this problem using machine learning techniques to quickly and accurately predict the solution-temperature, based on spectroscopic data. Our emphasis is on establishing a simple and practically useful solution to this problem. Our dataset comprises spectroscopic absorption data from both nanorods and nanobipyramids capped with PNIPAM, measured at discretely varied and pre-set temperature states. Specific regions of the spectroscopic data are selected as features for prediction using random forest (RF), gradient boosting (GB) and adaptive boosting (AB) regression techniques. Our prediction results indicate that RF and GB techniques can be used successfully to predict solution temperatures instantly to within 1 1C of accuracy.
关键词： PNIPAM,spectroscopic data,temperature prediction,adaptive boosting,machine learning,random forest,gradient boosting,gold nanoparticles
更新于2025-09-16 10:30:52