- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Variable selection for the determination of total polar materials in fried oils by near infrared spectroscopy
摘要： Total polar materials (TPM) content is considered as the best indicator and the most common parameter to check the quality of deep-frying oils. The development of simpler and quicker analytical techniques than the available methods to monitor oil quality in restaurants and fried food outlets is an important topic related to the human health. This paper reports a comparison of the variable selection of near infrared (NIR) spectra by multiple linear regression (MLR-NIR) with partial least squares (PLS-NIR) models for the quantification of TPM in fried vegetable oils. The use of PLS-NIR offers an alternative in laboratory bench equipment for the determination of TPM in oils employed for frying different kinds of foods with relative prediction errors of 6.5%, a coefficient of determination for prediction of 0.99 and a residual predictive deviation (RPD) of 9.2 when selected wavenumber intervals were employed. MLR-NIR allows the selection of a reduced number of wavenumber in order to develop low cost instruments to evaluate the frying oil quality. Based on the NIR signals at four wavenumbers, the relative prediction error was 12.1%, the coefficient of determination for prediction was 0.96 and the RPD was 5.0.
关键词： partial least squares,total polar materials,multiple linear regression,vegetable fried oils,Near infrared spectroscopy
更新于2025-09-23 15:21:01
-
Sharpening the VNIR and SWIR Bands of Sentinel-2A Imagery through Modified Selected and Synthesized Band Schemes
摘要： In this work, the bands of a Sentinel-2A image with spatial resolutions of 20 m and 60 m are sharpened to a spatial resolution of 10 m to obtain visible and near-infrared (VNIR) and shortwave infrared (SWIR) spectral bands with a spatial resolution of 10 m. In particular, we propose a two-step sharpening algorithm for Sentinel-2A imagery based on modified, selected, and synthesized band schemes using layer-stacked bands to sharpen Sentinel-2A images. The modified selected and synthesized band schemes proposed in this study extend the existing band schemes for sharpening Sentinel-2A images with spatial resolutions of 20 m and 60 m to improve the pan-sharpening accuracy by changing the combinations of bands used for multiple linear regression analysis through band-layer stacking. The proposed algorithms are applied to the pan-sharpening algorithm based on component substitution (CS) and a multiresolution analysis (MRA), and our results are then compared to the sharpening results when using sharpening algorithms based on existing band schemes. The experimental results show that the sharpening results from the proposed algorithm are improved in terms of the spatial and spectral properties when compared to existing methods. However, the results of the sharpening algorithm when applied to our modified band schemes show differing tendencies. With the modified, selected band scheme, the sharpening result when applying the CS-based algorithm is higher than the result when applying the MRA-based algorithm. However, the quality of the sharpening results when using the MRA-based algorithm with the modified synthesized band scheme is higher than that when using the CS-based algorithm.
关键词： band selection and synthesis,Sentinel-2A sharpening,multiple linear regression,component substitution (CS),multiresolution analysis (MRA)
更新于2025-09-19 17:15:36
-
[IEEE 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) - Waikoloa Village, HI (2018.6.10-2018.6.15)] 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) - A Fast Quasi-Static Time Series Simulation Method for PV Smart Inverters with VAR Control using Linear Sensitivity Model
摘要： Fast deployment of renewable energy resources in distribution networks, especially solar photovoltaic (PV) systems, have motivated the need for inverter-based voltage regulation. Integration studies are often necessary to fully understand the potential impacts of PV inverter settings on the various elements of the distribution system, including voltage regulators and capacitor banks. A year long quasi-static time series (QSTS) at second-level granularity provides a comprehensive assessment of these impacts, however the computational burden associated with running QSTS limits its applicability. This paper proposes a fast QSTS simulation technique capable of modeling the smart inverter dynamic VAR control functionality and accurately estimating the states of controllable elements including voltage regulators and capacitor banks at each time step. Consequently, the complex interactions between various legacy voltage regulation devices is also captured. The efficacy of the proposed algorithm is demonstrated on the IEEE 13-bus test case with a 98% reduction in computation time.
关键词： multiple linear regression,smart inverter,voltage control,Quasi-static time series,PV impact studies
更新于2025-09-19 17:13:59
-
[IEEE 2019 22nd International Conference on Electrical Machines and Systems (ICEMS) - Harbin, China (2019.8.11-2019.8.14)] 2019 22nd International Conference on Electrical Machines and Systems (ICEMS) - Power Forecasting of Photovoltaic Generation Based on Multiple Linear Regression Method with Real-time Correction Term
摘要： This paper proposes a photovoltaic power generation forecasting model which improves Multiple Linear Regression method (MLRM) with real-time correction term traditional day-ahead, hourly power (RCT). Firstly, a generation prediction model is developed by MLRM based on qualitative variables (hour, month, weather type), quantitative variable (solar radiation intensity) and physical characteristics of interactions between the variables. Secondly, an improved is presented which adds a model named MLRM+RCT correction term based on shorter real-time measured power data to MLRM to reduce the hourly prediction errors of MLRM. MLRM+RCT is tested based on power generation data released by IEEE Energy Forecasting Group in 2014. The results show that the performance of MLRM+RCT is better than that of MLRM and a benchmark method called exponential smoothing method.
关键词： Photovoltaic system,real-time correction term,Multiple Linear Regression method,short-term forecasting
更新于2025-09-19 17:13:59
-
[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 Comparison of Hyper-Sharpening Algorithms for Fusing VNIR and SWIR Bands of WorldView-3 Satellite Imagery
摘要： In this study, visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands of WorldView-3 imagery were sharpened to the spatial resolution of a panchromatic image. We performed experiments on three band schemes according to a method for generating an optimal panchromatic image. Various pan-sharpening algorithms based on component-substitution (CS) and multi-resolution analysis (MRA) were applied to each band scheme. Quantitative and qualitative assessments were performed using the quality indices and visual inspection.
关键词： selected and synthesized band scheme,multiple linear regression,WorldView-3 sharpening
更新于2025-09-09 09:28:46
-
HYPERSPECTRAL IMAGE DENOISING USING MULTIPLE LINEAR REGRESSION AND BIVARIATE SHRINKAGE WITH 2-D DUAL-TREE COMPLEX WAVELET IN THE SPECTRAL DERIVATIVE DOMAIN
摘要： In this paper, a new denoising method is proposed for hyperspectral remote sensing images, and tested on both the simulated and the real-life datacubes. Predicted datacube of the hyperspectral images is calculated by multiple linear regression in the spectral domain based on the strong spectral correlation of the useful signal and the inter-band uncorrelation of the random noise terms in hyperspectral images. A two dimensional dual-tree complex wavelet transform is performed in the spectral derivative domain, where the noise level is elevated temporarily to avoid signal deformation during the wavelet denoising, and then the bivariate shrinkage is used to shrink the wavelet coefficients. Simulated experimental results demonstrate that the proposed method obtains better results than the other denoising methods proposed in the reference, improves the signal to noise ratio up to 0.5dB to 10dB. The real-life data experiment shows that the proposed method is valid and effective.
关键词： denoising,Hyperspectral imagery,complex wavelet,bivariate shrinkage,multiple linear regression
更新于2025-09-09 09:28:46
-
Hourly PV production estimation by means of an exportable Multiple Linear Regression model
摘要： The current state of photovoltaic (PV) electricity integration is demanding several strategies that control the optimal performance of PV plants. Cleaning the PV plant, controlling PV production or the estimation of the electricity generation, are some relevant items related to the PV systems. In general, the soiling, the clouds and another climatological factors are involved in the final PV production. For knowing the performance of a PV system, it is necessity to model the PV plant behavior according to these relevant variables. In this work, a Multiple Linear Regression (MLR) model has been presented to determine the hourly PV production by using the Performance Ratio (PR) factor, according to different technologies: Cadmium Telluride (CdTe) and multicrystalline silicon (mc-Si). In this sense, data from several PV plants were studied in different Chile regions: San Pedro de Atacama and Antofagasta. With this study, it has been determined that the model can be extrapolated to different climatological emplacements, where generally, the root mean square error (RMSE) presents values lower than 16% in all cases, having the best result the CdTe technology.
关键词： Solar Energy,PV estimation,mc-Si,Multiple Linear Regression,CdTe,Performance Ratio
更新于2025-09-09 09:28:46
-
Sugar Contents and Firmness of Apples Based on Multi-Spectral Imaging Technology
摘要： The paper proposed a prediction method of apple sugar content and firmness based on multi-spectral imaging. Firstly, four characteristic wavelengths (670, 750, 780 and 810 nm) were selected by correlation coefficient method. The gray images of samples at different wavelengths were collected by multi-spectral imaging system, then fitted with Lorenz function, modified Lorenz function, Gaussian function and polynomial function, respectively. It was found that the fitting effect of modified Lorenz function was best. Therefore, the experiment was performed by multiple linear regression and partial least square regression analysis of sugar content and firmness with the fitting parameters of modified Lorenz function. The result showed that the prediction of multiple linear regression model was better than partial least squares regression model. The modeling correction correlation coefficient, calibration standard deviation, the prediction correlation coefficient and predicted standard deviation of sugar content were 0.8568, 0.6736, 0.8395 and 0.7068, respectively. The modeling correction correlation coefficient, calibration standard deviation, the prediction correlation coefficient and the predicted standard deviation of firmness were 0.8660, 0.3275, 0.8407 and 0.3555, respectively. The results also showed that this method was feasible for the prediction of apple sugar content and firmness.
关键词： Firmness,Multi-spectral imaging,Curve fitting,Apple,Multiple linear regression,Sugar content
更新于2025-09-04 15:30:14