- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Damage degree prediction method of CFRP structure based on fiber Bragg grating and epsilon-support vector regression
摘要： The assessment of structural damage is of great significance for ensuring the service safety of carbon fiber reinforced plastics (CFRP) structures. In this paper, the damage degree prediction method of CFRP structure based on fiber Bragg grating and epsilon-support vector regression was studied. The structural dynamic response signals were detected by fiber Bragg grating sensors. Then, the Fourier transform was used to extract the dynamic characteristics of the structure as the damage feature, and the damage feature dimensionality was reduced by using the RReliefF algorithm. On this basis, the damage degree prediction model of CFRP structure based on epsilon-support vector regression was established. Finally, the method proposed in this paper was experimentally verified. The results showed that the epsilon-support vector regression model can accurately predict the damage degree of unknown samples, and the absolute relative error of 27 experiments was less than 10% for 30 testing experiments. This paper provided a feasible method for predicting the damage degree of CFRP structures.
关键词： Frequency response,Carbon fiber reinforced plastics,Epsilon-support vector regression,RReliefF,Damage degree prediction
更新于2025-09-23 15:23:52
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Synergistic Use of Optical and Dual-Polarized SAR Data With Multiple Kernel Learning for Urban Impervious Surface Mapping
摘要： Accurate mapping of impervious surface distribution is important but challenging. Integrating optical and SAR data to improve urban impervious surface estimation has recently shown promising performance. Further investigation and development on this multisensory approach are conducted in this study. A novel multiple kernel learning (MKL) framework is proposed to integrate heterogeneous features from Landsat-8 and Sentinel-1A data effectively. A linearly weighted combination of basic kernels built using each group of features is learned as the optimal kernel, while the hyperparameters and the weight of each basic kernel are determined simultaneously by using the differential evolution algorithm. Then, the optimal kernel is embedded into the support vector regression algorithm, and the impervious surface abundance of the study area is estimated by applying the developed multiple kernel support vector regression (MKSVR) model. The impervious surface ground truth at a subpixel level is derived from a high-resolution image by means of object-oriented classification. The experimental results indicate that the synergistic use of optical and dual-pol SAR data by employing MKSVR achieves a noteworthy improvement for impervious surface estimation compared to that using optical image alone, the root mean square error is decreased by 4.30%, and the coefficient of determination (R2) is increased by 9.47%, and that the incorporation of optical and SAR does not guarantee the improved performance, simply stacking all features of multisource data into a vector is not a good choice, and the MKL is a powerful tool to apply as demonstrated by the experiments conducted in this study.
关键词： Landsat-8,Heterogeneous features,Sentinel-1A,multiple kernel support vector regression (MKSVR),impervious surface abundance
更新于2025-09-23 15:23:52
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Dynamic Behavioral Modeling of RF Power Amplifier Based on Time-Delay Support Vector Regression
摘要： A new, dynamic behavioral modeling technique, based on a time-delay support vector regression (SVR) method, is presented in this paper. As an advanced machine learning algorithm, the SVR method provides an effective option for behavioral modeling of radio frequency (RF) power amplifiers (PAs), taking into account the effects of both device nonlinearity and memory. The basic theory of the proposed modeling technique is given, along with a detailed model extraction procedure. Unlike traditional artificial neural network (ANN) techniques, which take time to determine the best configuration of the model, the SVR method can obtain the optimal model in short time, using the grid-search technique. An example of an optimal SVR model selection applied to an RF PA is also given; the performance of the selected model presents a big improvement when compared with the default SVR model. Experimental validation is performed using an LDMOS PA, a single device gallium nitride (GaN) PA, and a Doherty GaN PA, revealing that the new modeling methodology provides very efficient and extremely accurate prediction. Compared with traditional Volterra models, canonical piecewise linear models, and ANN-based models, the proposed SVR model gives improved performance with reasonable complexity. In addition, it is shown that the model can predict accurately the behavior of the PA under input power levels that are different from those under which it is extracted.
关键词： time delay,radio frequency (RF) power amplifiers (PAs),machine learning,Dynamic behavioral model,support vector regression (SVR)
更新于2025-09-23 15:23:52
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Local Feature Descriptor and Derivative Filters for Blind Image Quality Assessment
摘要： In this letter, a novel Blind Image Quality Assessment (BIQA) technique is introduced to provide an automatic and reproducible evaluation of distorted images. In the approach, the information carried by image derivatives of different orders is captured by local features and used for the image quality prediction. Since a typical local feature descriptor is designed to ensure a robust image patch representation, in this letter, a novel descriptor which additionally highlights local differences enhanced by the filtering is proposed. Furthermore, a set of derivative kernels is introduced. Finally, the support vector regression (SVR) technique is used to map statistics of described features into subjective scores, providing an objective quality score for an image. Extensive experimental validation on popular IQA image datasets reveals that the proposed method outperforms the state-of-the-art hand-crafted and deep learning BIQA measures.
关键词： Local features,Feature descriptor,Support vector regression,Derivative filters,Blind image quality assessment
更新于2025-09-23 15:22:29
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[IEEE 2018 18th Mediterranean Microwave Symposium (MMS) - Istanbul, Turkey (2018.10.31-2018.11.2)] 2018 18th Mediterranean Microwave Symposium (MMS) - PSO Based Approach to the Synthesis of a Cylindrical-Rectangular Ring Microstrip Conformal Antenna Using SVR Models with RBF and Wavelet Kernels
摘要： In this work, particle swarm optimization (PSO) based approach to the synthesis of a cylindrical-rectangular ring microstrip conformal antenna using support vector regression (SVR) models is presented. Resonant frequency of the antenna is obtained by PSO of trained SVR models. Radial basis function (RBF) and wavelet kernel functions are used in SVR models. Simulation examples are given and the results are compared.
关键词： support vector regression,rectangular ring microstrip antenna,particle swarm optimization,wavelet kernel,conformal antennas
更新于2025-09-19 17:15:36
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Improved measurement on quantitative analysis of coal properties using laser induced breakdown spectroscopy
摘要： It is of great significance to realize the rapid or online analysis of coal properties for combustion optimization of thermal power plants. In this work, a set of calibration schemes based on laser-induced breakdown spectroscopy (LIBS) was determined to improve the measurement on quantitative analysis of coal properties, including proximate analysis (calorific value, ash, volatile content) and ultimate analysis (carbon and hydrogen). Firstly, different normalization methods (channel normalization and normalization with the whole spectral area) combined with two regression algorithms (partial least-squares regression [PLSR] and support vector regression [SVR]) were compared to initially select the appropriate calibration method for each indicator. Then, the influence of de-noising by the wavelet threshold de-noising (WTD) on quantitative analysis was further studied, thereby the final analysis schemes for each indicator were determined. The results showed that WTD coupled SVR can be well estimated calorific value and ash, the root mean square error of prediction (RMSEP) were 0.80 MJ kg?1 and 0.60%. Coupling WTD and PLSR performed best for the measurement of volatile content, the RMSEP was 0.76%. For the quantitative analysis of carbon and hydrogen, normalization with the whole spectral area combined with SVR can get better measurement results, the RMSEP of the measurements were 1.08% and 0.21%, respectively. The corresponding average standard deviation (RSD) for calorific value, ash, volatile content, carbon and hydrogen of validation sets were 0.26 MJ kg?1, 0.57%, 0.79%, 0.47% and 0.08%, respectively. The results demonstrated that the selection of appropriate spectral pre-processing coupled with calibration strategies for each indicator can effectively improve the accuracy and precision of the measurement on coal properties.
关键词： partial least-squares regression (PLSR),quantitative analysis,normalization,Laser-induced breakdown spectroscopy (LIBS),coal properties,support vector regression (SVR),wavelet threshold de-noising (WTD)
更新于2025-09-19 17:13:59
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Machine Learning for Tailoring Optoelectronic Properties of Single-Walled Carbon Nanotube Films
摘要： A machine learning technique, namely support vector regression, is implemented to enhance single-walled carbon nanotube (SWCNT) thin-film performance for transparent and conducting applications. We collected a comprehensive dataset describing the influence of synthesis parameters (temperature and CO2 concentration) on the equivalent sheet resistance (at 90% transmittance in the visible light range) for SWCNT films obtained by a semi-industrial aerosol (floating-catalyst) CVD with CO as a carbon source and ferrocene as a catalyst precursor. The predictive model trained on the dataset shows principal applicability of the method for refining synthesis conditions towards the advanced optoelectronic performance of multi-parameter processes such as nanotube growth. Further doping of the improved carbon nanotube films with HAuCl4 results in the equivalent sheet resistance of 39 Ω/□ – one of the lowest values achieved so far for SWCNT films.
关键词： transparent conductive films,support vector regression,single-walled carbon nanotubes,optoelectronic properties,machine learning
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP) - Wuxi, China (2019.7.19-2019.7.21)] 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP) - Performance Monitoring of PAM4 Optical Communication System Based on Principal Component Analysis and Support Vector Regression
摘要： As a popular signal transmission technique, PAM4 is widely used short and medium distance optical communication networks. Though there exists techniques to monitor its performance, it’s still necessary to do more research on techniques for increasing resource utilization and monitoring effects in PAM4 optical network. In this paper, the experimental data of chromatic dispersive (CD) and optical signal to noise ratio (OSNR) under the condition of nonlinear optical fiber is generated by setting relevant parameters in PAM4 optical communication system we constructed. Then the eigenvectors of experimental data are obtained by constructing asynchronous amplitude histograms and we use principal component analysis technique to reduce the dimension of eigenvectors by 20.8%. Finally, the dimensionality reduction results are used as the input of support vector regression algorithm to complete the prediction of CD and OSNR. The prediction errors of CD and OSNR are varied in the range of -0.02 to 0.02 dB and -0.8 to 0.8 ps/nm respectively. The simulation results show that the proposed method is effective and accurate in monitoring the performance of PAM4 optical communication system and it can also be applied to monitor the performance of PAM-N optical communication system.
关键词： principal component analysis,PAM4,asynchronous amplitude histogram,support vector regression,performance monitoring
更新于2025-09-16 10:30:52
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Weighting Factor Selection of the Ensemble Model for Improving Forecast Accuracy of Photovoltaic Generating Resources
摘要： Among renewable energy sources, solar power is rapidly growing as a major power source for future power systems. However, solar power has uncertainty due to the effects of weather factors, and if the penetration rate of solar power in the future increases, it could reduce the reliability of the power system. A study of accurate solar power forecasting should be done to improve the stability of the power system operation. Using the empirical data from solar power plants in South Korea, the short-term forecasting of solar power outputs were carried out for 2016. We performed solar power forecasting with the support vector regression (SVR) model, the na?ve Bayes classifier (NBC), and the hourly regression model. We proposed the ensemble method including the selection of weighting factors for each model to improve forecasting accuracy. The forecasted solar power generation error was indicated using normalized mean absolute error (NMAE) compared to the plant capacity. For the ensemble method, the results of each forecasting model were weighted with the reciprocal of the standard deviation of the forecast error, thus improving the solar power outputs forecast accuracy.
关键词： support vector regression,na?ve Bayes classifier,solar power forecasting,machine learning,ensemble,day ahead power forecasting
更新于2025-09-11 14:15:04
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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 - A Model Driven Approach for Snow Wetness Retrieval with Sentinel-l
摘要： In this paper, a novel approach for the retrieval of snow wetness is presented for Sentinel-1 (S-1) data. The approach uses the information on snow proprieties provided by the hydroclimatological model AMUNDSEN and confirmed by comparisons performed at different sematic level to train a regressor that is able to exploit the dual-polarimetric information provided by S-1. The preliminary results obtained for the Rofental in Austria are discussed.
关键词： Support Vector Regression,snow wetness,physical snow model,SAR images
更新于2025-09-10 09:29:36