- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Coal Discrimination Analysis Using Tandem Laser-Induced Breakdown Spectroscopy and Laser Ablation Inductively Coupled Plasma Time-of-Flight Mass Spectrometry
摘要： The contribution and impact of combined laser ablation inductively coupled plasma time of flight mass spectrometry (LA-ICP-TOF-MS) and laser induced breakdown spectroscopy (LIBS) were evaluated for the discrimination analysis of different coal samples. This Tandem approach allows simultaneous determination of major and minor elements (C, H, Si, Ca, Al, Mg, etc), and trace elements (V, Ba, Pb, U, etc.) in the coal. The research focused on coal classification strategies based on principle component analysis (PCA) combined with K-means clustering, partial least squares discrimination analysis (PLS-DA), and support vector machine (SVM) for analytical performance. Correlation analyses performed from TOF mass and LIBS emission spectra from the coal samples showed that most major, minor, and trace elements emissions had negative correlation with the volatile content. Suitable variables for the classification models were determined from these data. The individual TOF data, LIBS data, and the combined data of TOF and LIBS, respectively, as the input for different models were analyzed and compared. In all cases, the results obtained with the combined TOF and LIBS data were found to be superior to those obtained with the individual TOF or LIBS data. The nonlinear SVM model combined with TOF and LIBS data provided the best coal classification performance, with a classification accuracy of up to 98%.
关键词： Principal component analysis,Support vector machine,Partial least squares discrimination analysis,Laser-induced breakdown spectroscopy,K-means clustering,Coal discrimination,Laser ablation inductively coupled plasma time of flight mass spectrometry
更新于2025-09-23 15:19:57
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Estimation of solar photovoltaic energy curtailment due to volta??watt control
摘要： Restoration is important in preprocessing hyperspectral images (HSI) to improve their visual quality and the accuracy in target detection or classification. In this paper, we propose a new low-rank spectral nonlocal approach (LRSNL) to the simultaneous removal of a mixture of different types of noises, such as Gaussian noises, salt and pepper impulse noises, and fixed-pattern noises including stripes and dead pixel lines. The low-rank (LR) property is exploited to obtain precleaned patches, which can then be better clustered in our spectral nonlocal method (SNL). The SNL method takes both spectral and spatial information into consideration to remove mixed noises as well as preserve the fine structures of images. Experiments on both synthetic and real data demonstrate that LRSNL, although simple, is an effective approach to the restoration of HSI.
关键词： nonlocal means,Hyperspectral image,spectral and spatial information,restoration,low rank (LR)
更新于2025-09-23 15:19:57
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M-NL: Robust NL-Means Approach for PolSAR Images Denoising
摘要： This letter proposes a new method for polarimetric synthetic aperture radar (PolSAR) denoising. More precisely, it seeks to address a new statistical approach for weights computation in nonlocal (NL) approaches. The aim is to present a simple criterion using M-estimators and to detect similar pixels in an image. A binary hypothesis test is used to select similar pixels which will be used for covariance matrix estimation together with associated weights. The method is then compared with an advanced state-of-the-art PolSAR denoising method named NL-SAR. The filter performances are measured by a set of different indicators, including relative errors on incoherent target decomposition parameters, coherences, polarimetric signatures, and edge preservation on a set of simulated PolSAR images. Finally, results for RADARSAT-2 PolSAR data are presented.
关键词： M-estimators,nonlocal (NL) means,Wishart distribution,polarimetric synthetic aperture radar (PolSAR),Detection
更新于2025-09-19 17:15:36
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[IEEE 2018 International Conference on Platform Technology and Service (PlatCon) - Jeju (2018.1.29-2018.1.31)] 2018 International Conference on Platform Technology and Service (PlatCon) - Classification of Daytime and Night Based on Intensity and Chromaticity in RGB Color Image
摘要： Classification of daytime and night in the color image is a very important task in image processing based on color images acquired from CCTV. Also, weather classification must be performed before performing image processing such as weather report, shadow removal and fog detection. In this paper, we proposed the classification, whether a color image is daytime or night. We first set the range of pixels in the gray level image from 0 to 50, from 51 and over 101, and we estimated each range as daytime, evening and night. In the first step, it is estimated based on the intensity and chromaticity of the image. If the classification result based on the intensity and chromaticity image is the same, the process is terminated. Otherwise, the k-means segmentation is used in the second step to determine the final classification. Some experiments are conducted so as to verify the proposed method, and the classification is well performed. The execution time results up to the first step are about 0.31 seconds on average, and the execution up to the second step is changed according to the resolution of the image.
关键词： daytime and night,k-means segmentation,intensity,classification,chromaticity
更新于2025-09-19 17:15:36
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Uniform Star Catalogue using GWKM Clustering for Application in Star Sensors
摘要： In this paper, a novel algorithm of weighted k-means clustering with geodesic criteria is presented to generate a uniform database for a star sensor. For this purpose, selecting the appropriate star catalogue and desirable minimum magnitude and eliminating double stars are among the steps of the uniformity process. Further, Delaunay triangulation and determining the scattered data density by using a Voronoi diagram were used to solve the problems of the proposed clustering method. Thus, by running a Monte Carlo simulation to count the number of stars observed in different fields of view, it was found that the uniformity leads to a significant reduction of the probability of observing a large number of stars in all fields of view. In contrast, the uniformity slightly increased the field of view needed to observe the minimum number of required stars for an identification algorithm.
关键词： Geodesic k-means clustering,Scattered data density,Delaunay triangulation,Optimized star catalogue
更新于2025-09-19 17:15:36
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3D Affine: An Embedding of Local Image Features for Viewpoint Invariance Using RGB-D Sensor Data
摘要： Local image features are invariant to in-plane rotations and robust to minor viewpoint changes. However, the current detectors and descriptors for local image features fail to accommodate out-of-plane rotations larger than 25°–30°. Invariance to such viewpoint changes is essential for numerous applications, including wide baseline matching, 6D pose estimation, and object reconstruction. In this study, we present a general embedding that wraps a detector/descriptor pair in order to increase viewpoint invariance by exploiting input depth maps. The proposed embedding locates smooth surfaces within the input RGB-D images and projects them into a viewpoint invariant representation, enabling the detection and description of more viewpoint invariant features. Our embedding can be utilized with different combinations of descriptor/detector pairs, according to the desired application. Using synthetic and real-world objects, we evaluated the viewpoint invariance of various detectors and descriptors, for both standalone and embedded approaches. While standalone local image features fail to accommodate average viewpoint changes beyond 33.3°, our proposed embedding boosted the viewpoint invariance to different levels, depending on the scene geometry. Objects with distinct surface discontinuities were on average invariant up to 52.8°, and the overall average for all evaluated datasets was 45.4°. Similarly, out of a total of 140 combinations involving 20 local image features and various objects with distinct surface discontinuities, only a single standalone local image feature exceeded the goal of 60° viewpoint difference in just two combinations, as compared with 19 different local image features succeeding in 73 combinations when wrapped in the proposed embedding. Furthermore, the proposed approach operates robustly in the presence of input depth noise, even that of low-cost commodity depth sensors, and well beyond.
关键词： nonparametric spherical k-means,3D points projection,6D pose estimation,viewpoint invariance,wide baseline matching,denoising and interpolation,local image feature embedding,out-of-plane rotations
更新于2025-09-19 17:15:36
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Means and covariances of photon numbers in multimode Gaussian states
摘要： In the field of continuous variables and particularly of Gaussian states an important role is played by the statistics of photon numbers. This issue has been considered in pioneering works for the one- and two-mode cases, but a systematic approach seems not to be available. In this paper, the most general multimode mixed Gaussian state, which is generated starting from a multimode thermal state and processed by the most general Gaussian unitary, is considered. In the N-mode the photon numbers are represented by N random variables, one for each mode, and the target is the evaluation of the means and of the covariances. The means describe statistically the amount of photon numbers in each mode, while the covariances give the correlations between the photon numbers in the different modes. For both means and covariances a closed-form result is obtained, expressed in simple and compact formulas. The main tools are provided by the representation of Gaussian unitaries (given by a cascade of an N-mode squeeze operator, an N-mode rotation operator, and a parallel set of N single-mode displacement operators) and by the corresponding Bogoliubov transformations.
关键词： means,photon numbers,multimode,covariances,Bogoliubov transformations,Gaussian states
更新于2025-09-19 17:15:36
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High-energy industrial 2D X-ray imaging system with effective nonlocal means denoising for nondestructive testing
摘要： High-energy industrial X-ray imaging systems are widely used in the field of nondestructive testing for the detection of defects in mechanical material. To improve the defect detection ratio, it is highly important to reduce the amount of noise in this process. The purpose of this study is to develop a nonlocal means denoising algorithm in order to evaluate noise characteristics in a 450 kVp high-energy industrial X-ray imaging system. The analysis approach is tested on two phantom images, and image performance is evaluated by visual assessment, as well as the normalized noise power spectrum, contrast to noise ratio, and coefficient of variation. Improvement in image performance is attributed to the use of NLM denoising algorithm on high-energy industrial X-ray images, and results demonstrate that the proposed algorithm effectively reduces image noise.
关键词： Nondestructive testing,Nonlocal means denoising algorithm,High-energy industrial X-ray imaging system,Quantitative performance evaluation.
更新于2025-09-19 17:15:36
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Grid Integration of Small-Scale Photovoltaic Systems in Secondary Distribution Network- A Review
摘要： Restoration is important in preprocessing hyperspectral images (HSI) to improve their visual quality and the accuracy in target detection or classification. In this paper, we propose a new low-rank spectral nonlocal approach (LRSNL) to the simultaneous removal of a mixture of different types of noises, such as Gaussian noises, salt and pepper impulse noises, and fixed-pattern noises including stripes and dead pixel lines. The low-rank (LR) property is exploited to obtain precleaned patches, which can then be better clustered in our spectral nonlocal method (SNL). The SNL method takes both spectral and spatial information into consideration to remove mixed noises as well as preserve the fine structures of images. Experiments on both synthetic and real data demonstrate that LRSNL, although simple, is an effective approach to the restoration of HSI.
关键词： nonlocal means,Hyperspectral image,spectral and spatial information,restoration,low rank (LR)
更新于2025-09-19 17:13:59
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Investigation of Dye-Sensitized Solar Cell With Photoanode Modified by TiOa??-ZnO Nanofibers
摘要： Restoration is important in preprocessing hyperspectral images (HSI) to improve their visual quality and the accuracy in target detection or classification. In this paper, we propose a new low-rank spectral nonlocal approach (LRSNL) to the simultaneous removal of a mixture of different types of noises, such as Gaussian noises, salt and pepper impulse noises, and fixed-pattern noises including stripes and dead pixel lines. The low-rank (LR) property is exploited to obtain precleaned patches, which can then be better clustered in our spectral nonlocal method (SNL). The SNL method takes both spectral and spatial information into consideration to remove mixed noises as well as preserve the fine structures of images. Experiments on both synthetic and real data demonstrate that LRSNL, although simple, is an effective approach to the restoration of HSI.
关键词： Hyperspectral image,nonlocal means,spectral and spatial information,restoration,low rank (LR)
更新于2025-09-19 17:13:59