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Статті в журналах з теми "Ceramics, multidimensional data analysis"

Автор: Grafiati

Опубліковано: 14 грудня 2024

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1

Puig-Castellví, Francesc, Yolanda Pérez, Benjamín Piña, Romà Tauler, and Ignacio Alfonso. "Compression of multidimensional NMR spectra allows a faster and more accurate analysis of complex samples." Chemical Communications 54, no. 25 (2018): 3090–93. http://dx.doi.org/10.1039/c7cc09891j.

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A variable-filtering approach of multidimensional NMR spectral data is proposed. Filtered data keeps resonance information and original spectral resolution. The denoised data can be easily peak-picked and integrated, allowing a fast and accurate NMR analysis.

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2

Boyd, C. Clifford, and Donna C. Boyd. "A Multidimensional Investigation of Biocultural Relationships among Three Late Prehistoric Societies in Tennessee." American Antiquity 56, no. 1 (January 1991): 75–88. http://dx.doi.org/10.2307/280974.

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Interrelations among three roughly contemporaneous late prehistoric Mississippian societies in Middle and East Tennessee are reexamined in terms of currently available biological, archaeological, and ethnohistoric data. Previous researchers have suggested a close relation between two of those cultures—Mouse Creek and Middle Cumberland—to the exclusion of the third, Dallas. However, multivariate analyses of craniofacial and mandibular dimensions of individuals from the three groups suggest a greater biological relation between Dallas and Mouse Creek than between Mouse Creek and Middle Cumberland. In addition, a comparison of intrasite settlement patterning, ceramic and mortuary variability, and ethnohistoric data across the three groups support the skeletal analysis. Relations between Dallas and Mouse Creek may mirror similar processes of sociopolitical reorganization occurring throughout the Southeast in the late prehistoric period.

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3

Munita, Casimiro S., Lúcia P. Barroso, and Paulo M. S. Oliveira. "Variable selection study using Procrustes analysis." Open Journal of Archaeometry 1, no. 1 (December 20, 2013): 7. http://dx.doi.org/10.4081/arc.2013.e7.

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Several analytical techniques are often used in archaeometric studies, and when used in combination, these techniques can be used to assess 30 or more elements. Multivariate statistical methods are frequently used to interpret archaeometric data, but their applications can be problematic or difficult to interpret due to the large number of variables. In general, the analyst first measures several variables, many of which may be found to be uninformative, this is naturally very time consuming and expensive. In subsequent studies the analyst may wish to measure fewer variables while attempting to minimize the loss of essential information. Such multidimensional data sets must be closely examined to draw useful information. This paper aims to describe and illustrate a stopping rule for the identification of redundant variables, and the selection of variables subsets, preserving multivariate data structure using Procrustes analysis, selecting those variables that are in some senses adequate for discrimination purposes. We provide an illustrative example of the procedure using a data set of 40 samples in which were determined the concentration of As, Ce, Cr, Eu, Fe, Hf, La, Na, Nd, Sc, Sm, Th, and U obtained via instrumental neutron activation analysis (INAA) on archaeological ceramic samples. The results showed that for this data set, only eight variables (As, Cr, Fe, Hf, La, Nd, Sm, and Th) are required to interpret the data without substantial loss information.

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4

Herr, Quentin, Alex Braun, Andrew Brownfield, Ed Rudman, Dan Dosch, Trent Josephsen, and Anna Herr. "Measurement and data-assisted simulation of bit error rate in RQL circuits." Superconductor Science and Technology 35, no. 2 (January 14, 2022): 025017. http://dx.doi.org/10.1088/1361-6668/ac45a1.

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Abstract A circuit-simulation-based method is used to determine the thermally-induced bit error rate of superconducting Single Flux Quantum logic circuits. Simulations are used to evaluate the multidimensional Gaussian integral across noise current sources attached to the active devices. The method is data-assisted and has predictive power. Measurement determines the value of a single parameter, effective noise bandwidth, for each error mechanism. The errors in the distributed networks of comparator-free Reciprocal Quantum Logic nucleate across multiple Josephson junctions, so the effective critical current is about three times that of the individual devices. The effective noise bandwidth is only 6%–23% of the junction plasma frequency at a modest clock rate of 3.4 GHz, which is 1% of the plasma frequency. This analysis shows the ways measured bit error rate comes out so much lower than simplistic estimates based on isolated devices.

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5

Barath, E., J. de Leeuw, W. Heiser, J. Meulman, and F. Critchley. "Multidimensional Data Analysis." Biometrics 45, no. 3 (September 1989): 1034. http://dx.doi.org/10.2307/2531708.

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6

Trajkovic, Dusan, Bratislav Mikaric, and Marija Markovic-Blagojevic. "Multidimensional data analysis." Trendovi u poslovanju 3, no. 2 (2015): 63–70. http://dx.doi.org/10.5937/trendpos1502063t.

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7

Iizuka, Masaya, Yuichi Mori, Tomoyuki Tarumi, and Yutaka Tanaka. "9. Multidimensional Data Analysis." Journal of the Japanese Society of Computational Statistics 15, no. 2 (2003): 337–45. http://dx.doi.org/10.5183/jjscs1988.15.2_337.

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8

Morita, Tsukio, and Yutaka Tanaka. "9. Multidimensional Data Analysis." Journal of the Japanese Society of Computational Statistics 15, no. 2 (2003): 347–55. http://dx.doi.org/10.5183/jjscs1988.15.2_347.

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9

Biebler, Karl-Ernst, Bernd J^|^auml;ger, Michael Wodny, and Elke Below. "9. Multidimensional Data Analysis." Journal of the Japanese Society of Computational Statistics 15, no. 2 (2003): 357–60. http://dx.doi.org/10.5183/jjscs1988.15.2_357.

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10

Tokairin, Tomoya, and Yoshiharu Sato. "9. Multidimensional Data Analysis." Journal of the Japanese Society of Computational Statistics 15, no. 2 (2003): 361–68. http://dx.doi.org/10.5183/jjscs1988.15.2_361.

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11

Naya, Futoshi, and Hiroshi Sawada. "From Multidimensional Mixture Data Analysis to Spatio-temporal Multidimensional Collective Data Analysis." NTT Technical Review 14, no. 2 (February 2016): 14–20. http://dx.doi.org/10.53829/ntr201602fa2.

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12

Hennhöfer, Oliver, Julian Bruns, Peter Ullrich, Andreas Heiß, Galibjon Sharipov, and Dimitrios Paraforos. "Multidimensional Exploratory Spatial Data Analysis." GI_Forum 1 (2021): 136–51. http://dx.doi.org/10.1553/giscience2021_02_s136.

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13

No authorship indicated. "Review of Multidimensional Data Analysis." Contemporary Psychology: A Journal of Reviews 33, no. 5 (May 1988): 462. http://dx.doi.org/10.1037/025753.

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14

O'Hayre, Ryan, Jake Huang, Meagan Papac, Yewon Shin, Youdong Kim, and Andriy Zakutayev. "(Invited) Accelerating Electrochemistry: The Development of Rapid Impedance Methods and High-Throughput Screening of Novel Oxide Electrodes for Fuel Cells and Electrolyzers." ECS Meeting Abstracts MA2022-02, no. 47 (October 9, 2022): 1738. http://dx.doi.org/10.1149/ma2022-02471738mtgabs.

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Анотація:

As electrochemical technologies rise to ever greater prominence in the global energy landscape, new techniques to accelerate the discovery, design, and characterization of novel electrochemical materials and devices are needed. Motivated by this opportunity, here we present our recent work in the development of novel rapid multi-dimensional impedance characterization using the Distribution of Relaxation Times (DRT) combined with high-throughput impedance-based screening of electrocatalysts for ceramic fuel cells and electrolyzers, both of which offer opportunities to accelerate electrochemical materials research and development. The DRT has been established as a versatile tool for analysis of electrochemical impedance spectroscopy (EIS) data, providing insight into the relevant electrochemical processes of fuel cells, batteries, and other devices without the constraints of an a priori model. Here, we present a method for obtaining the DRT directly from rapid time-domain measurements. This technique provides the DRT on a time-scale that is 1-2 orders of magnitude faster than conventional EIS measurements, and opens new avenues for characterization and analysis that are inaccessible and/or impractical with conventional EIS. We demonstrate how this technique can be used to construct multidimensional DRT maps as a function of variables such as applied bias, temperature, changing gas conditions, or state-of-charge (Figure 1). In addition, the model can be adapted to account for time-varying sample states, enabling meaningful analysis of unstable samples and transient phenomena such as equilibration or degradation processes. Finally, we demonstrate how hardware sampling-rate limitations can be overcome via hybrid frequency- and time-domain measurements to scan a broad range of timescales without sacrificing measurement speed. The capability of this method to resolve the DRT along additional dimensions promises to enhance the interpretability of the DRT and provide new insight to guide materials and device optimization. We then discuss how rapid impedance characterization and analysis methods can be coupled to combinatorial thin-film synthesis and high-throughput automated characterization to investigate a large family of catalytically-active triple-ionic-electronic conducting oxide perovskite materials based on the Ba(Co,Fe,Zr,Y)O3-δ (BCFZY) compositional system, which show great promise for catalyzing the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) for ceramic fuel cells and electrolyzers (Figure 2). In total, we collected and analyzed more than 2,500 impedance spectra from three combinatorial thin-film electrode libraries, comprising 432 distinct compositions. These libraries were synthesized by pulsed laser deposition and measured at three temperatures under two different gas atmospheres, enabling a new scientific insight into the trends governing electrochemical performance. Our combinatorial experiments demonstrate that Co-rich compositions achieve the lowest overall polarization resistance under both dry air and humid N2, while high Fe content may impede the performance at low-to-intermediate temperatures. Reuslts from the combinatorial experiments are supported by isotope-labled SIMS trace diffusion studies as well as by protonic-ceramic fuel cell and electrolysis cell testing of both symmetric cells and full cells that incorporated select compositions of interest. Hierarchical Bayesian analysis indicates that the performance-limiting process depends on the chemical composition, measurement temperature, and atmospheric humidity. Thus, by combining rapid analysis methods with combinatorial experimentation, we achieve a composition map of the condition-dependent electronic properties of materials in the BCFZY perovskite family for application as air electrodes in protonic ceramic fuel cell and electrolyzers. Figure 1

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15

Eliceiri, K. W., C. Rueden, W. A. Mohler, W. L. Hibbard, and J. G. White. "Analysis of Multidimensional Biological Image Data." BioTechniques 33, no. 6 (December 2002): 1268–73. http://dx.doi.org/10.2144/02336bt01.

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16

Bondarev, A. E., A. V. Bondarenko, and V. A. Galaktionov. "Visual analysis procedures for multidimensional data." Scientific Visualization 10, no. 4 (October 25, 2018): 120–33. http://dx.doi.org/10.26583/sv.10.4.09.

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17

Lex, A., M. Streit, C. Partl, Karl Kashofer, and Dieter Schmalstieg. "Comparative Analysis of Multidimensional, Quantitative Data." IEEE Transactions on Visualization and Computer Graphics 16, no. 6 (November 2010): 1027–35. http://dx.doi.org/10.1109/tvcg.2010.138.

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18

Fojud, A. "MULTIDIMENSIONAL DATA ANALYSIS IN CONSTRUCTION INDUSTRY." Statyba 6, no. 6 (January 2000): 431–35. http://dx.doi.org/10.1080/13921525.2000.10531626.

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19

Berge, Claude, Nicolas Froloff, Ravi Kiran Reddy Kalathur, Myriam Maumy, Olivier Poch, Wolfgang Raffelsberger, and Nicolas Wicker. "Multidimensional Fitting for Multivariate Data Analysis." Journal of Computational Biology 17, no. 5 (May 2010): 723–32. http://dx.doi.org/10.1089/cmb.2009.0126.

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20

Schuetz, Christoph G., Bernd Neumayr, Michael Schrefl, and Thomas Neuböck. "Reference Modeling for Data Analysis: The BIRD Approach." International Journal of Cooperative Information Systems 25, no. 02 (June 2016): 1650006. http://dx.doi.org/10.1142/s0218843016500064.

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Reference models for data analysis with data warehouses may consist of multidimensional reference models and analysis graphs. Multidimensional reference models are best-practice domain-specific data models for online analytical processing. Analysis graphs are reference models of analysis processes for event-driven data analysis. Small and medium-sized enterprises (SMEs) as well as large multinational companies may benefit from the use of reference models for data analysis. The availability of multidimensional reference models lowers the obstacles that inhibit SMEs from using business intelligence (BI) technology. Multinational companies may define multidimensional reference models for increased compliance among subsidiaries and departments. Furthermore, the definition of analysis graphs facilitates the handling of business events for both SMEs and large companies. Modelers may customize the chosen reference models, tailoring the models to the specific needs of the individual company or local subsidiary. Customizations may consist of additions, omissions, and modifications with respect to the reference model. In this paper, we propose a metamodel and customization approach for multidimensional reference models and analysis graphs. We specifically address the explicit modeling of key performance indicators as well as the definition of analysis situations and analysis graphs.

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21

Bondarev, A. E. "THE PROCEDURES OF VISUAL ANALYSIS FOR MULTIDIMENSIONAL DATA VOLUMES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W12 (May 9, 2019): 17–21. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w12-17-2019.

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<p><strong>Abstract.</strong> The paper is devoted to problems of visual analysis of multidimensional data sets using an approach based on the construction of elastic maps. This approach is quite suitable for processing and visualizing of multidimensional datasets. The elastic maps are used as the methods of original data points mapping to enclosed manifolds having less dimensionality. Diminishing the elasticity parameters one can design map surface which approximates the multidimensional dataset in question much better. Then the points of dataset in question are projected to the map. The extension of designed map to a flat plane allows one to get an insight about the structure of multidimensional dataset. The paper presents the results of applying elastic maps for visual analysis of multidimensional data sets of medical origin. Previously developed data processing procedures are applied to improve the results obtained - pre-filtering of data, removal of separated clusters (flotation), quasi-Zoom.</p>

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22

Bondarev, A. E., V. A. Galaktionov, and L. Z. Shapiro. "PROCESSING AND VISUAL ANALYSIS OF MULTIDIMENSIONAL DATA." Scientific Visualization 9, no. 5 (December 24, 2017): 86–104. http://dx.doi.org/10.26583/sv.9.5.08.

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23

Campbell, Akiko, Xiangbo Mao, Jian Pei, and Abdullah Al-Barakati. "Multidimensional Business Benchmarking Analysis on Data Warehouses." International Journal of Data Warehousing and Mining 13, no. 1 (January 2017): 51–75. http://dx.doi.org/10.4018/ijdwm.2017010103.

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Benchmarking analysis has been used extensively in industry for business analytics. Surprisingly, how to conduct benchmarking analysis efficiently over large data sets remains a technical problem untouched. In this paper, the authors formulate benchmark queries in the context of data warehousing and business intelligence, and develop a series of algorithms to answer benchmark queries efficiently. Their methods employ several interesting ideas and the state-of-the-art data cube computation techniques to reduce the number of aggregate cells that need to be computed and indexed. An empirical study using the TPC-H data sets and the Weather data set demonstrates the efficiency and scalability of their methods.

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24

Afanasiev, Alexander P., Vladimir E. Krivonozhko, Finn R. Førsund, and Andrey V. Lychev. "Multidimensional Visualization of Data Envelopment Analysis Models." Data Envelopment Analysis Journal 5, no. 2 (2021): 339–61. http://dx.doi.org/10.1561/103.00000040.

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25

Satława, Tadeusz, Joanna Grabska-Chrząstowska, and Przemysław Korohoda. "Application of multidimensional data analysis to chromatography." Image Processing & Communications 18, no. 2-3 (December 1, 2013): 101–8. http://dx.doi.org/10.2478/v10248-012-0084-1.

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Abstract This work presents analysis of chromatographic signal used to identify substances in samples. First part consists of chromatography overview and description of three classification methods (neural network with backpropagation, probabilistic neural network with Parzen window and support vector machines). Designed algorithm consists of several stages: signal filtering, peak detection and its approximation with sum of two Gaussian functions. The parameters of that two curves are the features vectors describing the peak of the substance. The last step is classification, for which two types of supervised machine learning were compared, based on the whole signal and on features vectors. Both types were tested for different classificators and their parameters. Verification was based on 55 chromatography signals. The best results for both methods of learning were achieved for probabilistic neural networks. The correct classification rate was 82% for the whole signal and 93% for feature vectors.

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26

Montaner, David, and Joaquín Dopazo. "Multidimensional Gene Set Analysis of Genomic Data." PLoS ONE 5, no. 4 (April 27, 2010): e10348. http://dx.doi.org/10.1371/journal.pone.0010348.

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27

Scavizzi, M. R., A. Elbhar, J. P. Fenelon, and F. D. Bronner. "Multidimensional analysis for interpreting antibiotic susceptibility data." Antimicrobial Agents and Chemotherapy 37, no. 4 (April 1, 1993): 929. http://dx.doi.org/10.1128/aac.37.4.929.

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28

Zhang, Yi, Teng Liu, Kefei Li, and Jiawan Zhang. "Improved visual correlation analysis for multidimensional data." Journal of Visual Languages & Computing 41 (August 2017): 121–32. http://dx.doi.org/10.1016/j.jvlc.2017.03.005.

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29

Ştefan, Raluca-Mariana, Mariuţa Şerban, and Costin Rudăreanu. "Multidimensional Data Analysis – Representation, Security and Management." Procedia Economics and Finance 16 (2014): 281–87. http://dx.doi.org/10.1016/s2212-5671(14)00802-8.

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30

Bouguettaya, A. "Data clustering analysis in a multidimensional space." Information Sciences 112, no. 1-4 (December 1998): 267–95. http://dx.doi.org/10.1016/s0020-0255(98)10037-3.

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31

Rychtáriková, Renata, Jan Korbel, Petr Macháček, Petr Císař, Jan Urban, and Dalibor Štys. "Point Information Gain and Multidimensional Data Analysis." Entropy 18, no. 10 (October 19, 2016): 372. http://dx.doi.org/10.3390/e18100372.

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32

Steven Perkins, W., and Thomas J. Reynolds. "Interpreting multidimensional data with cognitive differentiation analysis." Psychology and Marketing 12, no. 6 (September 1995): 481–99. http://dx.doi.org/10.1002/mar.4220120604.

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33

Qiu, Wenhai. "Application of Software Data Analysis Model Based on K-Means Clustering Algorithm." Security and Communication Networks 2022 (July 18, 2022): 1–7. http://dx.doi.org/10.1155/2022/4505814.

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Анотація:

In order to improve the anomaly detection ability of portable multidimensional control software test data, a software test data anomaly detection method based on K-means clustering is proposed. The abnormal data distribution structure model of portable multidimensional control software testing is constructed. The fuzzy semantic feature reconstruction method is adopted to identify the fuzzy parameters of portable multidimensional control software and extract the feature quantity of associated information. According to the evolution distribution of associated features, the joint combination feature analysis method is adopted to realize the fuzzy clustering center detection of abnormal data of portable multidimensional control software test, and the fusion of abnormal feature distribution is carried out to complete the joint multidimensional feature detection. The K-means clustering method is used for effective data combination control in portable multidimensional control software to extract and detect abnormal features of test data in portable multidimensional control software. Experimental results show that the accuracy of anomaly detection of software test data by the proposed method is always greater than 0.8. Conclusion. Using this method to detect abnormal data of portable multidimensional control software test has higher accuracy and better detection performance.

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34

A., Prarthana, Parag S., and A. Thomas. "Multidimensional Data Analysis Facilities and Challenges: A Survey for Data Analysis Tools." International Journal of Computer Applications 179, no. 13 (January 17, 2018): 28–33. http://dx.doi.org/10.5120/ijca2018916178.

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35

Bonilla-Marchán, Andrés Marcelo, Javier Alfredo Valdiviezo-Ortiz, Agnes Orosz, and Efstathios Stefos. "Undergraduate Students in Ecuador: A Data Analysis." Magis, Revista Internacional de Investigación en Educación 12, no. 25 (January 1, 2020): 187–204. http://dx.doi.org/10.11144/javeriana.m12-25.used.

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Анотація:

The aim of the present study was to identify the social characteristics of undergraduate students in Ecuador. Several analyses were carried out for this purpose; namely descriptive and multidimensional analyses. The descriptive analysis reveals the frequencies and percentages of the variables used in the study. The multidimensional analysis of multiple correspondences shows the differentiation criteria, and the hierarchical analysis classifies respondents based on their common characteristics. The results of this study reveal the characteristics of current undergraduate students in Ecuador and as such can help government and other higher educational authorities to develop future policies regarding undergraduate study in Ecuador.

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36

Zhang, Yi, Zhaoyang Xiong, and Jiawan Zhang. "Uncertainty-Aware Visual Correlation Analysis for Multidimensional Data." Journal of Computer-Aided Design & Computer Graphics 30, no. 6 (2018): 1089. http://dx.doi.org/10.3724/sp.j.1089.2018.16643.

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37

Catley, Christina, Kathy Smith, Carolyn McGregor, Andrew James, and J. Mikael Eklund. "A Framework for Multidimensional Real-Time Data Analysis." International Journal of Computational Models and Algorithms in Medicine 2, no. 1 (January 2011): 16–37. http://dx.doi.org/10.4018/jcmam.2011010102.

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Анотація:

In this paper, the authors present a framework to support multidimensional analysis of real-time physiological data streams and clinical data. The clinical context for the case study demonstration is neonatal intensive care, focusing specifically on the detection of episodes of central apnoea, a clinically significant problem. The model accounts for the multidimensional and real-time nature of apnoea of prematurity and the associated clinical rules. The framework demonstration includes: 1) defining rules that quantify concurrent behaviours between multiple synchronous data streams and asynchronous data values; 2) designing UML models to define present practice event processing for episodes of apnoea; 3) translating the model in SPADE to enable the deployment within the real-time processing layer of the Artemis platform, which utilizes IBM’s InfoSphere Streams; 4) demonstrating knowledge discovery with simple and complex temporal abstractions of the data streams; and 5) presenting results for early detection of episodes of apnoea across multiple physiological data streams.

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38

Peters, Stefan, and Liqiu Meng. "Visual Analysis for Nowcasting of Multidimensional Lightning Data." ISPRS International Journal of Geo-Information 2, no. 3 (August 26, 2013): 817–36. http://dx.doi.org/10.3390/ijgi2030817.

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39

ZOZOR, S., P. RAVIER, and O. BUTTELLI. "On Lempel–Ziv complexity for multidimensional data analysis." Physica A: Statistical Mechanics and its Applications 345, no. 1-2 (January 1, 2005): 285–302. http://dx.doi.org/10.1016/s0378-4371(04)00994-x.

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40

Flibotte, S., U. J. Hüttmeier, P. Bednarczyk, G. de France, B. Haas, P. Romain, Ch Theisen, J. P. Vivien та J. Zen. "Multidimensional analysis of high resolution γ-ray data". Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 320, № 1-2 (серпень 1992): 325–30. http://dx.doi.org/10.1016/0168-9002(92)90793-4.

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41

Ding, Cody. "Modeling Growth Data Using Multidimensional Scaling Profile Analysis." Quality & Quantity 41, no. 6 (October 10, 2006): 891–903. http://dx.doi.org/10.1007/s11135-006-9031-9.

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42

Zakharova, A. A., E. V. Vekhter, A. V. Shklyar, and A. J. Pak. "Visual modeling in an analysis of multidimensional data." Journal of Physics: Conference Series 944 (January 2018): 012127. http://dx.doi.org/10.1088/1742-6596/944/1/012127.

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43

Rychtáriková, Renata, Jan Korbel, Petr Macháček, and Dalibor Štys. "Point Divergence Gain and Multidimensional Data Sequences Analysis." Entropy 20, no. 2 (February 3, 2018): 106. http://dx.doi.org/10.3390/e20020106.

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44

Mastan Vali, Shaik, and P. Sujatha. "Multidimensional Data Analysis of Location Based Social Network." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 797. http://dx.doi.org/10.14419/ijet.v7i4.36.24534.

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Анотація:

Long range interpersonal communication benefits gather data on clients' social contacts, make an expansive interrelated informal organization, and open to clients how they are connected to others in the system. The basic of an OSN contains of customized client profiles, which for the most part encase interests (e.g. bought in intrigue gatherings), perceiving data (e.g. name and photograph), and individual contacts (e.g. rundown of connected clients, alleged "companions"). The ability to accumulate and inspect such information conveys particular chances to perceive the central belief systems of interpersonal organizations, their creation, movement and attributes. These sorts of informal communities are classified to be specific scholarly, general and area based interpersonal organizations. In this paper, we concentrated on the area based interpersonal organizations. Here, we investigations the diverse kinds of information that utilizations in area based interpersonal organizations and furthermore examine the effect of online datasets on neighborhood based interpersonal organization.

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45

Wang, Jianxin, and Geng Li. "Multidimensional meteorological data analysis based on machine learning." International Journal of Computer Applications in Technology 71, no. 3 (2023): 244–50. http://dx.doi.org/10.1504/ijcat.2023.10057372.

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46

Wang, Jianxin, and Geng Li. "Multidimensional meteorological data analysis based on machine learning." International Journal of Computer Applications in Technology 71, no. 3 (2023): 244–50. http://dx.doi.org/10.1504/ijcat.2023.132098.

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47

Bondarev, A. E. "VISUAL ANALYSIS OF TIME-VARYING MULTIDIMENSIONAL DATA SETS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W3-2023 (May 12, 2023): 41–45. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-w3-2023-41-2023.

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Abstract. This paper contains a description of computational experiments on the application of elastic maps to the analysis of time-varying volumes of textual information. Elastic maps are considered as a tool to provide analytical work with textual information and large information arrays of data. This paper presents the results of numerical experiments on the study of data volumes consisting of frequencies of joint use of words from different parts of speech, for instance “noun + verb” or “adjective + noun”. We consider text collections in Russian for experiments. Previously, static information arrays were mostly considered. It is for them methods of data analysis and methods of visual analytics were developed. Nevertheless, data comes in all the time in various areas of human activity. And in practice it is necessary to know how the cluster picture of multidimensional data volume changes over time. The paper describes the numerical experiments for real time-varying multidimensional data sets. Such experiments allows to analyze the evolution of cluster structure for multidimensional data and to trace the evolution for separate cluster.

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48

Hira, Swati, and P. S. Deshpande. "Data Analysis using Multidimensional Modeling, Statistical Analysis and Data Mining on Agriculture Parameters." Procedia Computer Science 54 (2015): 431–39. http://dx.doi.org/10.1016/j.procs.2015.06.050.

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Lin, Jing. "Design and Implementation of Short Messages Analysis System Based on Multidimensional Data Modeling." Applied Mechanics and Materials 130-134 (October 2011): 200–203. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.200.

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Multidimensional data modeling is the foundation of OLAP. This paper presents methods of multidimensional data modeling and applies it to the SMS(short messages) communication service. In order to analyze SMS data, a OLAP model must be designed. After the data from SMS communication are divided into fact data and dimension data in accordance with needs for SMS data analysis, the fact table and dimension tables are created. The multidimensional SMS data analysis model is built on the tables and then implemented by SQL server.

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Lainscsek, Claudia, Manuel E. Hernandez, Howard Poizner, and Terrence J. Sejnowski. "Delay Differential Analysis of Electroencephalographic Data." Neural Computation 27, no. 3 (March 2015): 615–27. http://dx.doi.org/10.1162/neco_a_00656.

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We propose a time-domain approach to detect frequencies, frequency couplings, and phases using nonlinear correlation functions. For frequency analysis, this approach is a multivariate extension of discrete Fourier transform, and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short and sparse time series and can be extended to cross-trial and cross-channel spectra (CTS) for electroencephalography data where multiple short data segments from multiple trials of the same experiment are available. There are two versions of CTS. The first one assumes some phase coherency across the trials, while the second one is independent of phase coherency. We demonstrate that the phase-dependent version is more consistent with event-related spectral perturbation analysis and traditional Morlet wavelet analysis. We show that CTS can be applied to short data windows and yields higher temporal resolution than traditional Morlet wavelet analysis. Furthermore, the CTS can be used to reconstruct the event-related potential using all linear components of the CTS.

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