- 标题
- 摘要
- 关键词
- 实验方案
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Classification of Sugar Beets Based on Hyperspectral and Extreme Learning Machine Methods
摘要： Sugar beet varieties were classified based on hyperspectral technology and the Extreme Learning Machine (ELM) algorithm. The influences of seven pretreatment methods, namely, Savitzky-Golay smoothing (SG), the first derivative (FD) method, SG smoothing combined with the FD method (SG-FD), logarithmic transformation (LT), LT combined with the FD method (LT-FD), the standard normal variate (SNV) method, and SNV combined with the FD method (SNV-FD), on the recognition performance of the ELM model were analyzed to select the best pretreatment method. To simplify the input variables, the standard deviation peak method was used to extract the feature bands for different preprocessed spectral data. The experimental results showed that for different pretreatment methods, the recognition rates of sugar beet varieties by ELM models were all over 80%. Additionally, the combination of different pretreatment methods and FD effectively improved the signal-to-noise ratio and enhanced the accuracy and stability of spectral models. Overall, the recognition accuracy of the ELM models established based on the feature bands was better than that established based on all bands, which suggests that the feature bands extracted by the standard deviation peak method are effective. Based on the SG-FD pretreatment method, the ELM models established using all bands and feature bands both achieved the highest recognition effect. Specifically, the recognition rates of the prediction sets were 93.94% and 95.45%, respectively.
关键词： Standard deviation peak method,ELM,Different pretreatment methods,Sugar beet variety,Hyperspectral
更新于2025-09-23 15:23:52
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Optical Wireless Communication Based Indoor Positioning Algorithms: Performance Optimisation and Mathematical Modelling
摘要： In this paper, the performance of the optimal beam radius indoor positioning (OBRIP) and two-receiver indoor positioning (TRIP) algorithms are analysed by varying system parameters in the presence of an indoor optical wireless channel modelled in line of sight configuration. From all the conducted simulations, the minimum average error value obtained for TRIP is 0.61 m against 0.81 m obtained for OBRIP for room dimensions of 10 m × 10 m × 3 m. In addition, for each simulated condition, TRIP, which uses two receivers, outperforms OBRIP and reduces position estimation error up to 30%. To get a better understanding of error in position estimation for different combinations of beam radius and separation between light emitting diodes, the 90th percentile error is determined using a cumulative distribution frequency (CDF) plot, which gives an error value of 0.94 m for TRIP as compared to 1.20 m obtained for OBRIP. Both algorithms also prove to be robust towards change in receiver tilting angle, thus providing flexibility in the selection of the parameters to adapt to any indoor environment. In addition, in this paper, a mathematical model based on the concept of raw moments is used to confirm the findings of the simulation results for the proposed algorithms. Using this mathematical model, closed-form expressions are derived for standard deviation of uniformly distributed points in an optical wireless communication based indoor positioning system with circular and rectangular beam shapes.
关键词： position estimation,channel modelling,raw moments,optical wireless communication,indoor positioning system,cumulative distribution frequency,standard deviation
更新于2025-09-23 15:22:29
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Statistical classification of contamination in glass insulators by reading its spectrum
摘要： This paper proposes to use a statistical approach for classifying the contamination level of insulators used in high voltage transmission lines. This studied case considers the voltage applied on glass insulators used in a chain of six elements of a 69 kV power line. When submitted to high voltage, the insulators radiate radio frequency signals up to frequencies of some GHz. In this frequency range the signal can be detected using portable antennas, and then be applied to post processing. These received spectra are analyzed by statistical methods, mean and standard deviation, and the cross relation between them is used to classify the pollution level in insulators. To reduce complexity and cost a simple data processing using the mean and the standard deviation are used. Two types of glass insulators are considered: clean or polluted, and to cover the entire frequency range from 30 MHz to 1 GHz two types of antennas were needed. The results obtained with the mean and the standard deviation show that the first 100 MHz bandwidth can be used for statistical of the insulator pollution level.
关键词： radio frequency spectrum,mean value,High voltage transmission lines insulators,statistical classification methods,standard deviation
更新于2025-09-23 15:22:29
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Influence of laser wavelength on self-absorption effect in laser-induced breakdown spectroscopy
摘要： Self-absorption effect seriously affects the accuracy of the quantitative analysis in laser-induced breakdown spectroscopy (LIBS), confining the further development of the technology. In this study, three laser wavelengths at ultraviolet (355 nm), visible light (532 nm), and infrared (1064 nm) were selected to study the influence of laser wavelength on self-absorption effect in LIBS. The effect of the laser wavelength the on spectral stability was also investigated. The results showed that when the wavelength was 1064 nm compared with the wavelength of 355 nm and 532 nm, the full widths at half maximum (FWHMs) of aluminum (Al) I 396.2 nm were reduced by 39% and 23%, the FWHM of potassium (K) I 766.5 nm were reduced by 58% and 22%. In addition, the laser wavelength was also closely related to the stability of the spectral intensity. In comparison with the laser wavelengths of 532 nm and 1064 nm, the best spectral repeatability occurred when the laser wavelength was 355 nm. The relative standard deviation (RSD) of the K I 766.5 nm was only 2.5%. The results indicate that laser wavelength affects the self-absorption effect and spectral stability of LIBS, which means selecting an appropriate laser wavelength can reduce self-absorption and improve stability.
关键词： Laser-induced Breakdown Spectroscopy,Self-absorption,Laser wavelength,Relative standard deviation
更新于2025-09-16 10:30:52
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[IEEE 2018 International Russian Automation Conference (RusAutoCon) - Sochi (2018.9.9-2018.9.16)] 2018 International Russian Automation Conference (RusAutoCon) - Object Hierarchy in a Digital Image
摘要： The paper describes a model of binary hierarchical clustering of image pixels for object detection. In the model, a hierarchical sequence (a hierarchy) of pixel clusters is obtained adaptively to an image by iterative merging of pixel sets. Clustering of pixels depending on the number of clusters is given by a hierarchy of piecewise-constant approximations of the image and is described by a convex sequence of corresponding values of the total quadratic error, which is minimized for a given number of clusters. Due to the convexity property, the pixel clusters and their colors in the image are ordered by the absolute value of the increment of the total squared error accompanied by the dividing of cluster in two parts. For the hierarchy of pixel clusters, the problem of unambiguous assignment of image points to detected objects is formalized. In this case, the output of object detection is a sequence of object associations that incrementally reveal or disappear on a certain background. Objects are detected in accordance with the threshold value of the number of pixels in the cluster, or the threshold for the increment of the total squared error, or by other pixel cluster attributes that have a sense of a quantitative measure. The hierarchy of pixel clusters and the hierarchy of object associations are encoded with "pixel rating" stereo pair and "object rating" stereo pair. The pilot experimental results are demonstrated.
关键词： digital image,minimization,Ward’s pixel clustering,piecewise constant approximations,standard deviation,K-means method
更新于2025-09-10 09:29:36