Analyzing the effect of the incidence angle on chlorophyll fluorescence intensity based on laser-induced fluorescence lidar

Transformer fault diagnosis research based on LIF technology and IAO optimization of LightGBM

Transformer fault diagnosis is a necessary operation to ensure the stable operation of a power system. In view of the problems of the low diagnostic rate and long time needed in traditional methods, such as the dissolved gas in oil method, a laser-induced fluorescence (LIF) spectral technology is proposed in this paper, which incorporated an improved aquila optimizer (IAO) and light gradient boosting machine (LightGBM), to predict the types of transformer faults. The original AO was improved using the Nelder Mead (NM) simple search method and opposition-based learning (OBL) mechanism, which could improve the parameter optimization ability of the model. Normal oil, thermal fault oil, local moisture oil, and electrical fault oil were selected as experimental samples. First, the spectral images of the four oil samples were obtained by LIF technology, and the fluorescence spectral curves obtained were preprocessed by multivariate scattering correction (MSC) and normalization (normalize), while kernel-based principle component analysis (KPCA) was used for dimensional reduction. The dimensionality-reduced data were then imported into the LightGBM model for training, and the IAO algorithm was used to optimize the parameters of the LightGBM. Finally, the experiment showed that the LIF technology demonstrated good recognition of the fault types for transformer fault diagnosis; the data purity after MSC preprocessing was higher than that of other processing methods; the prediction effect of the LightGBM model was superior to other prediction models; the LightGBM model optimized by IAO had better convergence, parameter optimization ability, and prediction accuracy than the LightGBM model optimized by the original AO and particle swarm optimization (PSO). Among the models, the MSC-IAO-LightGBM model had the best effect on fault prediction, with the mean square error (MSE) reaching 9.0643 × 10-7, mean absolute error (MAE) reaching 8.7439 × 10-4, and goodness of fit (R2) approaching 1. It can be implemented as a new diagnostic method in transformer fault detection, which is of great significance to ensure the stable and safe operation of power systems.

Comb-based multispectral LiDAR providing reflectance and distance spectra

Multispectral LiDAR enables joint observations of the 3D geometry and material properties of natural targets by combining ToF-based distance measurements with remote spectroscopy. Established multispectral LiDAR solutions provide mm-level range resolution and reflectance estimates of the target material over some tens of spectral channels. We propose a novel multispectral LiDAR approach based on an ultra-broadband frequency comb that enables enhanced remote spectroscopy by resolving relative delays in addition to reflectance. The spectrally-resolved delay and power measurements are transformed into distance and reflectance spectra by differential observations to a common reference object and adequate system calibration. These distance and reflectance spectra encode material information related to the surface and sub-surface composition and small-scale geometry. We develop the proposed comb-based multispectral LiDAR on an implementation covering the spectral range between 580 nm and 900 nm on 2 different spectral configurations with 7 and 33 channels of different spectral width. The performance assessment of the implemented system demonstrates a distance measurement precision better than 0.1 mm on most channels. Table-top probing results on five material specimens show that both the distance and the reflectance spectra alone enable discrimination of material specimens, while the novel distance signature particularly complements reflectance and increases classification accuracy when the material surface exhibits significant reflectance inhomogeneity. Material classification results using a support vector machine with radial basis function kernel demonstrate the potential of this approach for enhanced material classification by combining both signature dimensions.

Analyzing the effect of incident angle on echo intensity acquired by hyperspectral lidar based on the Lambert-Beckman model

Hyperspectral light detection and ranging (HSL) can acquire the spatial and spectral information simultaneously, which can provide more information than hyperspectral imaging and single band lidar. However, the echo intensity from targets is influenced by incident angle, and relative studies were still limited which result in the effect of incident angle on HSL not being completely understood. In this study, the incident angle effect in the whole band of HSL was analyzed and corrected. Then, five types of vegetation sample with different spectral characteristics were collected at the leaf level. Spectral range changing from 550 to 830 nm with a 1 nm spectral resolution was obtained. Lambert-Beckman model was applied to analyze the effect of the incident angle on the echo intensity. The experimental results demonstrated that the Lambert-Beckman model can efficiently apply in fitting the changing of echo intensity with incidence angle and efficiently eliminate the specular effect of target. In addition, the coefficient of variation ratio is significantly improved compared to the reference target-based model. The results illustrated that, compared to reference target-based model, the Lambert-Beckman model can efficiently explain and correct the incident angle effect with specular reflection in HSL. In addition, it was found that the specular fraction Ks, which is reduced with the increasing of reflectance, is dominating the incident angle effect in the whole band, while roughness m keeps stable at different wavelengths. Thus, this research will provide notably advanced insight into correcting the echo intensity of HSL.

To Research the Effects of Storage Time on Autotransfusion based on Erythrocyte Oxygen-Carrying Capacity and Oxidative Damage Characteristics

Autotransfusion refers to a blood transfusion method in which the blood or blood components of the patient are collected under certain conditions, returned to himself when the patient needs surgery or emergency after a series of storing and processing. Although autotransfusion can avoid blood-borne diseases and adverse reactions related to allogeneic blood transfusion, a series of structural and functional changes of erythrocytes will occur during extension of storage time, thus affecting the efficacy of clinical blood transfusion. Our research was aimed to explore the change of erythrocyte oxygen-carrying capacity in different storage time, such as effective oxygen uptake (Q), P50, 2,3-DPG, Na+-K+-ATPase, to detect membrane potential, the change of Ca2+, and reactive oxygen species (ROS) change of erythrocytes. At the same time, Western blot was used to detect the expression of Mitofusin 1 (Mfn1) and Mitofusin 2 (Mfn2) proteins on the cytomembrane, from the perspective of oxidative stress to explore the function change of erythrocytes after different storage time. This study is expected to provide experimental data for further clarifying the functional status of erythrocytes with different preservation time in patients with autotransfusion, achieving accurate infusion of erythrocytes and improving the therapeutic effect of autologous blood transfusion, which has important clinical application value.

Comparison of hourly aerosol retrievals from JAXA Himawari/AHI in version 3.0 and a simple customized method

Advanced Himawari imager (AHI) carried on the new-generation geostationary meteorological Himawari-8 satellite of Japan has been generating aerosol observations with a high temporal resolution since 7 July 2015. However, the previous studies lack a comprehensive quality assessment and spatial coverage analysis of AHI hourly aerosol products (level 3 version 3.0) across the full disk scan. The monitoring accuracy of different AHI aerosol products (AOD_pure and AOD_merged) and a simple customized product (AOD_mean) was evaluated against Aerosol Robotic Network (AERONET) and Maritime Aerosol Network (MAN) observations from May 2016 to February 2019 in this study. Results showed that AHI AOD_mean demonstrates a better agreement to AERONET AOD measurements than AOD_pure and AOD_merged over land (R = 0.81, bias =  - 0.011) and all the AHI land retrievals present a significant regional performance differences, while the relatively better performance is observed in AOD_merged over the coastal regions (R = 0.89, bias = 0.053). Over ocean, AHI exhibited overall overestimation in retrieving AOD against MAN observations and the relatively lower uncertainties were found in AOD_pure retrievals (R = 0.96, bias = 0.057). The hourly comparisons in different AHI products demonstrated a robust performance in the late afternoon (16:00-17:00 LT) over land and around the noon (10:00-13:00 LT) over coast. AHI AOD products indicated an obvious underestimation when compared to MODIS AOD retrievals over both land and ocean. Furthermore, the performance differences of AHI AOD products have also affected by the vegetation cover, pollution levels and relative humidity. For spatiotemporal coverage, the results of different AHI products demonstrated that AOD_mean can achieve relatively higher coverage than AOD_pure and AOD_merged, and AHI retrievals present significant regional differences in coverage capability.

Estimating leaf chlorophyll content by laser-induced fluorescence technology at different viewing zenith angles

Leaf chlorophyll content (LCC) is a key indicator of a plant's physiological status. Fast and non-destructive monitoring of chlorophyll content in plants through remote sensing is very important for accurate diagnosis and assessment of plant growth. Through the use of laser-induced fluorescence (LIF) technology, this study aims to compare the predictive ability of different single fluorescence characteristic and fluorescence characteristic combinations at various viewing zenith angles (VZAs) combined with multivariate analysis algorithms, such as principal component analysis (PCA) and support vector machine (SVM), for estimating the LCC of plants. The SVM models of LCC estimation were proposed, and fluorescence characteristics-fluorescence peak (FP), fluorescence ratio (FR), PCA, and first-derivative (FD) parameter-and fluorescence characteristic combinations (FP+FR, FP+FD, FR+FD, FP+FR+FD) were used as input variables for the models. Experimental results demonstrated that the effect of single fluorescence characteristics on the predictive performance of SVM models was: FR>FD>FP>PCA. Compared with other models, 0° SVM was the optimal model for estimating LCC by higher R². The fluorescence spectra and FD spectra observed at 0° and 30° were superior to those observed at 15°, 45°, and 60°. Thus, appropriate VZA must also be considered, as it can improve the accuracy of LCC monitoring. In addition, compared with single fluorescence characteristic, the FP+FR+FD was the optimal combination of fluorescence characteristics to estimate the LCC for the SVM model by higher R², indicating better predictive performance. The experimental results show that the combination of LIF technology and multivariate analysis can be effectively used for LCC monitoring and has broad development prospects.

Improvement in hourly PM2.5 estimations for the Beijing-Tianjin-Hebei region by introducing an aerosol modeling product from MASINGAR

This study improves traditional PM_2.5 estimation models by combining an hourly aerosol optical depth from the Advanced Himawari Imager onboard Himawari-8 with a newly introduced predictor to estimate hourly PM_2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region from November 1, 2018 to October 31, 2019. The new predictor is an hourly PM_2.5 forecasting product from the Model of Aerosol Species IN the Global AtmospheRe (MASINGAR). Comparative experiments were conducted by utilizing three extensively used regression models, namely, multiple linear regression (MLR), geographically weighted regression (GWR), and linear mixed effects (LME). A ten-fold cross validation (CV) demonstrated that the MASINGAR product significantly improved the performances of these models. The introduced product increased the model's determination coefficients (from 0.316 to 0.379 for MLR, from 0.393 to 0.445 for GWR, and from 0.718 to 0.765 for LME), decreased their root mean square errors (from 38.2 μg/m³ to 36.4 μg/m³ for MLR, from 36.0 μg/m³ to 34.4 μg/m³ for GWR, and from 24.5 μg/m³ to 22.4 μg/m³ for LME) and mean absolute errors (from 25.2 μg/m³ to 23.3 μg/m³ for MLR, from 23.5 μg/m³ to 21.8 μg/m³ for GWR, and from 15.2 μg/m³ to 13.7 μg/m³ for LME). Then, a well-trained LME model was utilized to estimate the spatial distributions of hourly PM_2.5 concentrations. Highly polluted localities were clustered in the central and southern areas of the BTH region, and the least polluted area was in northwestern Hebei. Seasonal PM_2.5 levels averaged from the hourly estimations exhibited the highest concentrations (55.4 ± 56.8 μg/m³) in the winter and lowest concentrations (25.1 ± 18.2 μg/m³) in the summer. MAIN FINDING: Introducing the PM_2.5 products from MASINGAR can significantly improve the performance of traditional models for surface PM_2.5 estimations by 7-20%.

Active 3D Imaging of Vegetation based on Multi-Wavelength Fluorescence LiDAR

Comprehensive and accurate vegetation monitoring is required in forestry and agricultural applications. The optical remote sensing method could be a solution. However, the traditional light detection and ranging (LiDAR) scans a surface to create point clouds and provide only 3D-state information. Active laser-induced fluorescence (LIF) only measures the photosynthesis and biochemical status of vegetation and lacks information about spatial structures. In this work, we present a new Multi-Wavelength Fluorescence LiDAR (MWFL) system. The system extended the multi-channel fluorescence detection of LIF on the basis of the LiDAR scanning and ranging mechanism. Based on the principle prototype of the MWFL system, we carried out vegetation-monitoring experiments in the laboratory. The results showed that MWFL simultaneously acquires the 3D spatial structure and physiological states for precision vegetation monitoring. Laboratory experiments on interior scenes verified the system's performance. Fluorescence point cloud classification results were evaluated at four wavelengths and by comparing them with normal vectors, to assess the MWFL system capabilities. The overall classification accuracy and Kappa coefficient increased from 70.7% and 0.17 at the single wavelength to 88.9% and 0.75 at four wavelengths. The overall classification accuracy and Kappa coefficient improved from 76.2% and 0.29 at the normal vectors to 92.5% and 0.84 at the normal vectors with four wavelengths. The study demonstrated that active 3D fluorescence imaging of vegetation based on the MWFL system has a great application potential in the field of remote sensing detection and vegetation monitoring.

Hyperspectral lidar point cloud segmentation based on geometric and spectral information

Light detection and ranging (lidar) can record a 3D environment as point clouds, which are unstructured and difficult to process efficiently. Point cloud segmentation is an effective technology to solve this problem and plays a significant role in various applications, such as forestry management and 3D building reconstruction. The spectral information from images could improve the segmentation result, but suffers from the varying illumination conditions and the registration problem. New hyperspectral lidar sensor systems can solve these problems, with the capacity to obtain spectral and geometric information simultaneously. The former segmentation on hyperspectral lidar were mainly based on spectral information. The geometric segmentation method widely used by single wavelength lidar was not employed for hyperspectral lidar yet. This study aims to fill this gap by proposing a hyperspectral lidar segmentation method with three stages. First, Connected-Component Labeling (CCL) using the geometric information is employed for base segmentation. Second, the output components of the first stage are split by the spectral difference using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Third, the components of the second stage are merged based on the spectral similarity using Spectral Angle Match (SAM). Two indoor experimental scenes were setup for validation. We compared the performance of our mothed with that of the 3D and intensity feature based method. The quantitative analysis indicated that, our proposed method improved the point-weighted score by 19.35% and 18.65% in two experimental scenes, respectively. These results showed that the geometric segmentation method for single wavelength lidar could be combined with the spectral information, and contribute to the more effective hyperspectral lidar point cloud segmentation.

Selection of the optimal bands of first-derivative fluorescence characteristics for leaf nitrogen concentration estimation

Laser-induced fluorescence technology provides a nondestructive and rapid method for monitoring leaf nitrogen concentration (LNC) based on its optical characteristics. Crop growth status can be efficiently diagnosed and quality evaluated by monitoring LNC. In this study, the first-derivative fluorescence spectrum (FDFS) was proposed and calculated based on the fluorescence spectra excited by 355, 460, and 556 nm excitation lights for rice LNC estimation. Then, the performance of each band FDFS characteristics and the FDFS ratio for LNC estimation were comprehensively discussed using principal component analysis and backpropagation neural network (BPNN). We analyzed the number of FDFS characteristics' influence on the accuracy of LNC monitoring. Results showed that R² does not clearly improve for the LNC monitoring based on the BPNN model when the number of extracted FDFS features exceeds 4 or 5. Therefore, the FDFS optimal band combination of different excitation light wavelengths mentioned was selected for LNC monitoring. The selected band combinations contained the majority of FDFS characteristics and could effectively be applied in monitoring LNC (for 355, 460, and 556 nm excitation lights, with R² of 0.764, 0.625, and 0.738, respectively) based on the BPNN model.