Strategies for EELS Data Analysis. Introducing UMAP and HDBSCAN for Dimensionality Reduction and Clustering

Hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and uniform manifold approximation and projection (UMAP), two new state-of-the-art algorithms for clustering analysis, and dimensionality reduction, respectively, are proposed for the segmentation of core-loss electron energy loss spectroscopy (EELS) spectrum images. The performances of UMAP and HDBSCAN are systematically compared to the other clustering analysis approaches used in EELS in the literature using a known synthetic dataset. Better results are found for these new approaches. Furthermore, UMAP and HDBSCAN are showcased in a real experimental dataset from a core–shell nanoparticle of iron and manganese oxides, as well as the triple combination nonnegative matrix factorization–UMAP–HDBSCAN. The results obtained indicate how the complementary use of different combinations may be beneficial in a real-case scenario to attain a complete picture, as different algorithms highlight different aspects of the dataset studied.

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Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis

Energies ◽

10.3390/en12040617 ◽

2019 ◽

Vol 12 (4) ◽

pp. 617 ◽

Cited By ~ 4

Author(s):

Gyul Lee ◽

Do-In Kim ◽

Seon Kim ◽

Yong-June Shin

Keyword(s):

Power Systems ◽

Dimensionality Reduction ◽

Data Compression ◽

Clustering Analysis ◽

Spatial Clustering ◽

Significant Risk ◽

Reduction Process ◽

Principal Component ◽

Wide Area ◽

Measurement Unit

This paper presents a multiscale phasor measurement unit (PMU) data-compression method based on clustering analysis of wide-area power systems. PMU data collected from wide-area power systems involve local characteristics that are significant risk factors when applying dimensionality-reduction-based data compression. Therefore, density-based spatial clustering of applications with noise (DBSCAN) is proposed for the preconditioning of PMU data, except for bad data and the automatic segmentation of correlated local datasets. Clustered PMU datasets of a local area are then compressed using multiscale principal component analysis (MSPCA). When applying MSPCA, each PMU signal is decomposed into frequency sub-bands using wavelet decomposition, approximation matrix, and detail matrices. The detail matrices in high-frequency sub-bands are compressed by using a PCA-based linear-dimensionality reduction process. The effectiveness of DBSCAN for data compression is verified by application of the proposed technique to the real-world PMU voltage and frequency data. In addition, comparisons are made with existing compression techniques in wide-area power systems.

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Quantitative Electron Energy Loss Mapping

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100118898 ◽

1985 ◽

Vol 43 ◽

pp. 404-405

Author(s):

R.D. Leapman ◽

C.R. Swyt

Keyword(s):

Energy Loss ◽

Electron Energy ◽

Electron Energy Loss Spectroscopy ◽

Elemental Concentration ◽

Core Loss ◽

Elemental Distribution ◽

Electron Energy Loss

The intensity of a characteristic electron energy loss spectroscopy (EELS) image does not, in general, directly reflect the elemental concentration. In fact, the raw core loss image can give a misleading impression of the elemental distribution. This is because the measured core edge signal depends on the amount of plural scattering which can vary significantly from region to region in a sample. Here, we show how the method for quantifying spectra due to Egerton et al. can be extended to maps.

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Spatial clustering analysis of green economy based on knowledge graph

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219074 ◽

2021 ◽

pp. 1-10

Author(s):

Shiyuan Zhou ◽

Xiaoqin Yang ◽

Qianli Chang

Keyword(s):

Spatial Data ◽

Clustering Analysis ◽

Spatial Clustering ◽

Evaluation Model ◽

Principal Component ◽

Green Economy ◽

Membership Degree ◽

Two Dimensional ◽

Dimensional Graph ◽

Improved Algorithm

By organically combining principal component analysis, spatial autocorrelation algorithm and two-dimensional graph theory clustering algorithm, the comprehensive evaluation model of regional green economy is explored and established. Based on the evaluation index system of regional green economy, this paper evaluates the development of regional green economy comprehensively by using principal component analysis, and evaluates the competitive advantage of green economy and analyzes the spatial autocorrelation based on the evaluation results. Finally, the green economy and local index score as observed values, by using the method of two-dimensional graph clustering analysis of spatial clustering. In view of the fuzzy k –modes cluster membership degree measure method without considering the defects of the spatial distribution of object, double the distance and density measurement of measure method is introduced into the fuzzy algorithm of k –modes, thus in a more reasonable way to update the membership degree of the object. Vote, MUSH-ROOM and ZOO data sets in UCI machine learning library were used for testing, and the F value of the improved algorithm was better than that of the previous one, indicating that the improved algorithm had good clustering effect. Finally, the improved algorithm is applied to the spatial data collected from Baidu Map to cluster, and a good clustering result is obtained, which shows the feasibility and effectiveness of the algorithm applied to spatial data. Results show that the development of green economy using the analysis method of combining quantitative analysis and qualitative analysis, explores the connotation of green economy with space evaluation model is feasible, small make up for the qualitative analysis of the green economy in the past, can objective system to reflect the regional green economic development level, will help policy makers scientific formulating regional economic development strategy, green integrated development of regional green economy from the macroscopic Angle, the development of network system.

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In Situ Analysis of Surface Contaminant Desorption during Low-Temperature Silicon Substrate Cleaning using Reflection Electron Energy Loss Spectrometry

MRS Proceedings ◽

10.1557/proc-259-449 ◽

1992 ◽

Vol 259 ◽

Cited By ~ 1

Author(s):

Selmer S. Wong ◽

Shouleh Nikzad ◽

Channing C. Ahn ◽

Aimee L. Smith ◽

Harry A. Atwater

Keyword(s):

Low Temperature ◽

Energy Loss ◽

Electron Energy ◽

Core Loss ◽

Temperature Dependent ◽

Electron Energy Loss Spectrometry ◽

Analysis Technique ◽

Chemical Analysis Technique ◽

Electron Energy Loss

ABSTRACTWe have employed reflection electron energy loss spectrometry (REELS), a surface chemical analysis technique, in order to analyze contaminant coverages at the submonolayer level during low-temperature in situ cleaning of hydrogen-terminated Si(100). The chemical composition of the surface was analyzed by measurements of the C K, O K and Si L2,3 core loss intensities at various stages of the cleaning. These results were quantified using SiC(100) and SiO2 as reference standards for C and O coverage. Room temperature REELS core loss intensity analysis after sample insertion reveals carbon at fractional monolayer coverage. We have established the REELS detection limit for carbon coverage to be 5±2% of a monolayer. A study of temperature-dependent hydrocarbon desorption from hydrogen-terminated Si(100) reveals the absence of carbon on the surface at temperatures greater than 200°C. This indicates the feasibility of epitaxial growth following an in situ low-temperature cleaning and also indicates the power of REELS as an in situ technique for assessment of surface cleanliness.

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Ultrasound-Assisted Citric Acid Extraction of Zinc in Plateau Red Soil and Tessier Speciation Study

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.726-731.4464 ◽

2013 ◽

Vol 726-731 ◽

pp. 4464-4467

Author(s):

Wei Wei ◽

Xue Jin Zhou ◽

Yun Tao Gao

Keyword(s):

Heavy Metal ◽

Citric Acid ◽

Manganese Oxides ◽

Red Soil ◽

Extraction Rate ◽

Acid Extraction ◽

Speciation Study ◽

Ultrasonic Assisted ◽

Ultrasound Assisted ◽

Iron And Manganese

Taking plateau red soil as research object, using the ultrasonic-assisted organic acid extraction the heavy metal zinc in it, and analyze the form of zinc. Results showed that the extraction rate can reach 68%, with the increase of time, the extraction effect of zinc is obviously enhanced in this method. Ultrasonic-assisted citric acid extraction soil can increase the extraction rate of exchangeable, bound to carbonates and bound to iron and manganese oxides relatively.

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Image Formation Based on Atomic Resolution Core-loss Electron Energy Loss Spectroscopy

Microscopy and Microanalysis ◽

10.1017/s1431927606062416 ◽

2006 ◽

Vol 12 (S02) ◽

pp. 1138-1139

Author(s):

MP Oxley ◽

K van Benthem ◽

M Varela ◽

SD Findlay ◽

LJ Allen ◽

...

Keyword(s):

Energy Loss ◽

Electron Energy ◽

Electron Energy Loss Spectroscopy ◽

Core Loss ◽

Image Formation ◽

Atomic Resolution ◽

Electron Energy Loss

Extended abstract of a paper presented at Microscopy and Microanalysis 2006 in Chicago, Illinois, USA, July 30 – August 3, 2006

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Enhancing Cd(II) Adsorption on Rice Straw Biochar by Modification of Iron and Manganese Oxides

SSRN Electronic Journal ◽

10.2139/ssrn.3958698 ◽

2021 ◽

Author(s):

Wen-Tao TAN ◽

Hang ZHOU ◽

Shang-Feng TANG ◽

Peng ZENG ◽

Jiao-Feng GU ◽

...

Keyword(s):

Rice Straw ◽

Manganese Oxides ◽

Rice Straw Biochar ◽

Iron And Manganese

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Adaptive Structure Concept Factorization for Multiview Clustering

Neural Computation ◽

10.1162/neco_a_01055 ◽

2018 ◽

Vol 30 (4) ◽

pp. 1080-1103 ◽

Cited By ~ 10

Author(s):

Kun Zhan ◽

Jinhui Shi ◽

Jing Wang ◽

Haibo Wang ◽

Yuange Xie

Keyword(s):

Nonnegative Matrix Factorization ◽

State Of The Art ◽

Nonnegative Matrix ◽

Adaptive Method ◽

Data Sets ◽

Clustering Methods ◽

Normalized Mutual Information ◽

Adaptive Structure ◽

Concept Factorization ◽

Multiview Clustering

Most existing multiview clustering methods require that graph matrices in different views are computed beforehand and that each graph is obtained independently. However, this requirement ignores the correlation between multiple views. In this letter, we tackle the problem of multiview clustering by jointly optimizing the graph matrix to make full use of the data correlation between views. With the interview correlation, a concept factorization–based multiview clustering method is developed for data integration, and the adaptive method correlates the affinity weights of all views. This method differs from nonnegative matrix factorization–based clustering methods in that it can be applicable to data sets containing negative values. Experiments are conducted to demonstrate the effectiveness of the proposed method in comparison with state-of-the-art approaches in terms of accuracy, normalized mutual information, and purity.

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