Готові списки джерел за темами / Data reduction and analysis

Зміст

  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Добірка наукової літератури з теми "Data reduction and analysis"

Автор: Grafiati
Опубліковано: 20 квітня 2024

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Статті в журналах з теми "Data reduction and analysis"

1

Liu, Songbin, Xiaomeng Huang, Haohuan Fu, Guangwen Yang, and Zhenya Song. "Data Reduction Analysis for Climate Data Sets." International Journal of Parallel Programming 43, no. 3 (October 18, 2013): 508–27. http://dx.doi.org/10.1007/s10766-013-0287-0.

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2

Niebling, Gerhard, Robert Aigner, Dietrich Wabner, and Reinhard Menzel. "Data reduction for curve analysis." Sensors and Actuators B: Chemical 34, no. 1-3 (August 1996): 481–86. http://dx.doi.org/10.1016/s0925-4005(97)80020-2.

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3

Kitamura, Akihiro, Sinya Nakamichi, and Takashi Okada. "ICONE15-10590 DATA ANALYSIS ON GLOVEBOX SIZE REDUCTION ACTIVITY IN GLOVEBOX DISMANTLING FACILITY." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2007.15 (2007): _ICONE1510. http://dx.doi.org/10.1299/jsmeicone.2007.15._icone1510_318.

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4

Amirteimoori, Alireza, Dimitris K. Despotis, and Sohrab Kordrostami. "Variables reduction in data envelopment analysis." Optimization 63, no. 5 (May 4, 2012): 735–45. http://dx.doi.org/10.1080/02331934.2012.684354.

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ROMERO, Julian, Luis DIAGO, Junichi SHINODA, and Ichiro HAGIWARA. "Comparison of Data Reduction Methods for the Analysis of Iyashi Expressions using Brain Signals." Journal of Advanced Simulation in Science and Engineering 2, no. 2 (2015): 349–66. http://dx.doi.org/10.15748/jasse.2.349.

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Li, S., N. Marsaglia, C. Garth, J. Woodring, J. Clyne, and H. Childs. "Data Reduction Techniques for Simulation, Visualization and Data Analysis." Computer Graphics Forum 37, no. 6 (March 30, 2018): 422–47. http://dx.doi.org/10.1111/cgf.13336.

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7

Peron, L. "Statistical analysis of sensory profiling data: data reduction and generalised Procrustes analysis." Food Quality and Preference 11, no. 1-2 (January 2000): 155–57. http://dx.doi.org/10.1016/s0950-3293(99)00070-1.

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Sarlin, Peter. "Data and dimension reduction for visual financial performance analysis." Information Visualization 14, no. 2 (October 10, 2013): 148–67. http://dx.doi.org/10.1177/1473871613504102.

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Анотація:
This article assesses the suitability of data and dimension reduction methods, and data–dimension reduction combinations, for visual financial performance analysis. Motivated by no comparable quantitative measure of all aspects of dimension reductions, this article attempts to capture the suitability of methods for the task through a qualitative comparison and illustrative experiments. While the discussion deals with differences of data–dimension reduction combinations in terms of their properties, the experiments illustrate their general applicability for financial performance analysis. The main conclusion is that topology-preserving data–dimension reduction combinations with predefined, regular grid shapes, such as the self-organizing map, are ideal tools for this task. We illustrate advantages of these types of methods with a visual financial performance analysis of large European banks.
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Neves, Julio Cesar, and Raad Qassim. "Supplier base reduction using data envelopment analysis." International Journal of Management and Decision Making 5, no. 1 (2004): 59. http://dx.doi.org/10.1504/ijmdm.2004.005009.

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Raux, J. "Supernova photometric data analysis and reduction details." New Astronomy Reviews 48, no. 7-8 (May 2004): 641–45. http://dx.doi.org/10.1016/j.newar.2003.12.041.

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Дисертації з теми "Data reduction and analysis"

1

Wehrhahn, Ansgar. "Data Reduction and Analysis for Exoplanet Characterization." Licentiate thesis, Uppsala universitet, Institutionen för fysik och astronomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-437613.

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Vamulapalli, Harika Rao. "On Dimensionality Reduction of Data." ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/1211.

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Random projection method is one of the important tools for the dimensionality reduction of data which can be made efficient with strong error guarantees. In this thesis, we focus on linear transforms of high dimensional data to the low dimensional space satisfying the Johnson-Lindenstrauss lemma. In addition, we also prove some theoretical results relating to the projections that are of interest when applying them in practical applications. We show how the technique can be applied to synthetic data with probabilistic guarantee on the pairwise distance. The connection between dimensionality reduction and compressed sensing is also discussed.
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McClelland, Robyn L. "Regression based variable clustering for data reduction /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/9611.

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Dhungana, Prakash. "Application for quick reduction of GPS data for urban mobility analysis." Master's thesis, Escola Superior Tecnologia e Gestão de Oliveira do Hospital, 2014. http://hdl.handle.net/10400.26/17521.

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Анотація:
The potential requirement of using data reduction application as an assistive tool for the student and researchers has been designed, developed and deployed. For that, numerous researches have been carried for this project. During the process, several modifications on architectural design, development and implemention have been done. The idea of further advancement of this application to the further development has been explored. Primarily, open source software for data reductions have been developed to suit the needs of the data analysis enthusiastic. Requirement for the project were drawn upon a thematic analysis carried on the data collected via the statement of arts and supervisors. Six evaluation sessions were carried out with various sectors of data reduction techniques to establish the acceptance, suitability, usefulness and efficiency of the product. The results were outstanding and indicate strong prospects of the product for the targeted arena.
The potential requirement of using data reduction application as an assistive tool for the student and researchers has been designed, developed and deployed. For that, numerous researches have been carried for this project. During the process, several modifications on architectural design, development and implemention have been done. The idea of further advancement of this application to the further development has been explored. Primarily, open source software for data reductions have been developed to suit the needs of the data analysis enthusiastic. Requirement for the project were drawn upon a thematic analysis carried on the data collected via the statement of arts and supervisors. Six evaluation sessions were carried out with various sectors of data reduction techniques to establish the acceptance, suitability, usefulness and efficiency of the product. The results were outstanding and indicate strong prospects of the product for the targeted arena.
info:eu-repo/semantics/acceptedVersion
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5

Ross, Ian. "Nonlinear dimensionality reduction methods in climate data analysis." Thesis, University of Bristol, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492479.

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Анотація:
Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These hnear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have been developed recently that may provide a potentially useful tool for the identification of low-dimensional manifolds in climate data sets arising from nonlinear dynamics. In this thesis I apply three such techniques to the study of El Niño/Southern Oscillation variability in tropical Pacific sea surface temperatures and thermocline depth, comparing observational data with simulations from coupled atmosphere-ocean general circulation models from the CMIP3 multi-model ensemble.
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Li, Yingxing. "On sliced methods in dimension reduction." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31559256.

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Li, Yingxing, and 李迎星. "On sliced methods in dimension reduction." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B31559256.

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Claudet, Andre Aman. "Data reduction for high speed computational analysis of three dimensional coordinate measurement data." Thesis, Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/17617.

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Tobeck, Daniel. "Data Structures and Reduction Techniques for Fire Tests." Thesis, University of Canterbury. Civil Engineering, 2007. http://hdl.handle.net/10092/1578.

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Анотація:
To perform fire engineering analysis, data on how an object or group of objects burn is almost always needed. This data should be collected and stored in a logical and complete fashion to allow for meaningful analysis later. This thesis details the design of a new fire test Data Base Management System (DBMS) termed UCFIRE which was built to overcome the limitations of existing fire test DBMS and was based primarily on the FDMS 2.0 and FIREBASEXML specifications. The UCFIRE DBMS is currently the most comprehensive and extensible DBMS available in the fire engineering community and can store the following test types: Cone Calorimeter, Furniture Calorimeter, Room/Corner Test, LIFT and Ignitability Apparatus Tests. Any data reduction which is performed on this fire test data should be done in an entirely mechanistic fashion rather than rely on human intuition which is subjective. Currently no other DBMS allows for the semi-automation of the data reduction process. A number of pertinent data reduction algorithms were investigated and incorporated into the UCFIRE DBMS. An ASP.NET Web Service (WEBFIRE) was built to reduce the bandwidth required to exchange fire test information between the UCFIRE DBMS and a UCFIRE document stored on a web server. A number of Mass Loss Rate (MLR) algorithms were investigated and it was found that the Savitzky-Golay filtering algorithm offered the best performance. This algorithm had to be further modified to autonomously filter other noisy events that occurred during the fire tests. This algorithm was then evaluated on test data from exemplar Furniture Calorimeter and Cone Calorimeter tests. The LIFT test standard (ASTM E 1321-97a) requires its ignition and flame spread data to be scrutinised but does not state how to do this. To meet these requirements the fundamentals of linear regression were reviewed and an algorithm to mechanistically scrutinise ignition and flame spread data was developed. This algorithm seemed to produce reasonable results when used on exemplar ignition and flame spread test data.
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Ferrer, Samuel. "STAIRS : Data reduction strategy on genomics." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-383465.

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Анотація:
Background. An enormous accumulation of genomic data has been taking place over the last ten years. This makes the activities of visualization and manual inspection, key steps in trying to understand large datasets containing DNA sequences with millions of letters. This situation has created a gap between data complexity and qualified personnel due to the need of trading between visualization, reduction capacity and exploratory functions, features rarely achieved by existing tools, such as SRA toolkit (https://www.ncbi.nlm.nih.gov/sra/docs/toolkitsoft/), for instance. A novel approach to the problem of genomic analysis and visualization was pursued in this project, by means of STrAtified Interspersed Reduction Structures (STAIRS). Result. Ten weeks of intense work resulted in novel algorithms to compress data, transform it into stairs vectors and align them. Smith–Waterman and Needleman–Wunsch algorithms have been specially modified for this purpose and the application brought about statistical performance and behavioural charts.
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Книги з теми "Data reduction and analysis"

1

Koide, Yoshiyuki. Data reduction in the lead isotope analysis. Misasa, Japan: Institute for Study of the Earth's Interior, Okayama University, 1990.

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Sears, John A. Analysis of an imperfect information flow reduction and sorting system. Monterey, Calif: Naval Postgraduate School, 1997.

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3

Landgrebe, D. A. Hyperspectral data analysis procedures with reduced sensitivity to noise. [Washington, D.C: National Aeronautics and Space Administration, 1993.

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4

Landgrebe, D. A. Hyperspectral data analysis procedures with reduced sensitivity to noise. [Washington, D.C: National Aeronautics and Space Administration, 1993.

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5

Gorban, Alexander N., and Dirk Roose, eds. Coping with Complexity: Model Reduction and Data Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-14941-2.

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Dirk, Roose, and SpringerLink (Online service), eds. Coping with Complexity: Model Reduction and Data Analysis. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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7

Bevington, Philip R. Data reduction and error analysis for thephysical sciences. 2nd ed. New York: McGraw-Hill, 1994.

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8

Keith, Robinson D., ed. Data reduction and error analysis for the physical sciences. 3rd ed. Boston: McGraw-Hill, 2003.

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9

Keith, Robinson D., ed. Data reduction and error analysis for the physical sciences. 2nd ed. New York: McGraw-Hill, 1992.

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10

1940-, Piper W. Stephen, Mick Mark W, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. Understanding the market for geographic information: A market segmentation and characteristics analysis. [Washington, DC]: National Aeronautics and Space Administration, Scientific and Technical Information Program, 1994.

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Частини книг з теми "Data reduction and analysis"

1

Durstewitz, Daniel. "Dimensionality Reduction." In Advanced Data Analysis in Neuroscience, 105–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59976-2_6.

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Phillips, Jeff M. "Dimensionality Reduction." In Mathematical Foundations for Data Analysis, 143–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62341-8_7.

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Boussouf, M., and M. Quafafou. "Data Reduction via Conflicting Data Analysis." In Lecture Notes in Computer Science, 139–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-39963-1_15.

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Middleton, James A. "Data Reduction: Principal Components Analysis." In Experimental Statistics and Data Analysis for Mechanical and Aerospace Engineers, 515–50. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003094227-16.

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Nokeri, Tshepo Chris. "Dimension Reduction and Multivariate Analysis Using Linear Discriminant Analysis." In Data Science Revealed, 117–28. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6870-4_6.

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Hamaker, J. P., R. H. Harten, G. N. J. van Diepen, and K. Kombrink. "Dwarf: The Dwingeloo-Westerbork Astronomical Reduction Facility." In Data Analysis in Astronomy, 449–55. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4615-9433-8_40.

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Misuraca, Michelangelo, Germana Scepi, and Maria Spano. "Network-Based Dimensionality Reduction for Textual Datasets." In Models for Data Analysis, 175–90. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15885-8_12.

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Gisbrecht, Andrej, Daniela Hofmann, and Barbara Hammer. "Discriminative Dimensionality Reduction Mappings." In Advances in Intelligent Data Analysis XI, 126–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34156-4_13.

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Mathar, Rudolf, Gholamreza Alirezaei, Emilio Balda, and Arash Behboodi. "Dimensionality Reduction." In Fundamentals of Data Analytics, 45–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56831-3_4.

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Grabelsky, D. A., S. M. Matz, J. Pendleton, W. R. Purcell, M. P. Ulmer, W. N. Johnson, and M. S. Strickman. "IGORE: Interactive GRO/OSSE Reduction Environment." In Data Analysis in Astronomy IV, 295–302. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3388-7_30.

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Тези доповідей конференцій з теми "Data reduction and analysis"

1

Paioro, L., B. M. Garilli, M. Scodeggio, and P. Franzetti. "The Data Reduction and Analysis Graphical Organizer." In ADA-III - Astronomical Data Analysis III Conference. BCS Learning & Development, 2004. http://dx.doi.org/10.14236/ewic/ada-iii2004.13.

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Fichtel, C. E., D. L. Bertsch, R. Hartman, S. D. Hunter, D. J. Thompson, B. L. Dingus, J. Esposito, et al. "EGRET data reduction and analysis." In The second Compton symposium. AIP, 1994. http://dx.doi.org/10.1063/1.45562.

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Garilli, B., P. Franzetti, L. Paioro, M. Scodeggio, and A. Zanichelli. "Spectroscopic surveys: a different approach to data reduction." In ADA-III - Astronomical Data Analysis III Conference. BCS Learning & Development, 2004. http://dx.doi.org/10.14236/ewic/ada-iii2004.10.

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Musso, C., and N. Oudjane. "Data reduction for particle filters." In Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis. IEEE, 2005. http://dx.doi.org/10.1109/ispa.2005.195383.

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Lu, Yu, Xu-Dong Liu, Jing-Jing Song, Tai-Hua Xu, and Eric C. C. Tsang. "Data Noise Suppression-based Attribute Reduction." In 2022 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2022. http://dx.doi.org/10.1109/icwapr56446.2022.9947188.

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Nichols, Coleman, Megan Hickman Fulp, Nathan DeBardeleben, and Jon C. Calhoun. "Exploring Data Reduction Techniques for Additive Manufacturing Analysis." In 2022 IEEE/ACM 8th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD). IEEE, 2022. http://dx.doi.org/10.1109/drbsd56682.2022.00008.

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Ghojogh, Benyamin, and Mark Crowley. "Principal Sample Analysis for Data Reduction." In 2018 IEEE International Conference on Big Knowledge (ICBK). IEEE, 2018. http://dx.doi.org/10.1109/icbk.2018.00054.

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Grosset, Pascal, Christopher M. Biwer, Jesus Pulido, Arvind T. Mohan, Ayan Biswas, John Patchett, Terece L. Turton, David H. Rogers, Daniel Livescu, and James Ahrens. "Foresight: Analysis That Matters for Data Reduction." In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2020. http://dx.doi.org/10.1109/sc41405.2020.00087.

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Jin, Zheming, and Jeffrey S. Vetter. "Evaluating Nonuniform Reduction in HIP and SYCL on GPUs." In 2022 IEEE/ACM 8th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD). IEEE, 2022. http://dx.doi.org/10.1109/drbsd56682.2022.00010.

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Laguardia, Joel, Dimas A. Dutra, and Ricardo Luiz U. Pinto. "Data Reduction Methods for Dutch-Roll Analysis." In AIAA Atmospheric Flight Mechanics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-0938.

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Звіти організацій з теми "Data reduction and analysis"

1

EDGEMON, G. L. Data Analysis and reduction in Hanford's corrosion monitoring systems. Office of Scientific and Technical Information (OSTI), June 1999. http://dx.doi.org/10.2172/782428.

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Cheng, Yi-Wen, and Christian L. Sargent. Data-reduction and analysis procedures used in NIST's thermomechanical processing research. Gaithersburg, MD: National Institute of Standards and Technology, 1990. http://dx.doi.org/10.6028/nist.ir.3950.

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Slaski, Lisa. Analysis of Mountaintop Data Reductions. Fort Belvoir, VA: Defense Technical Information Center, August 1997. http://dx.doi.org/10.21236/ada328824.

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Storage, Tara M., Robert A. Brockman, and Kevin M. Tienda. Analysis of Data Reduction Strategy used in TA Instruments Q800 DMA Test System. Fort Belvoir, VA: Defense Technical Information Center, July 2013. http://dx.doi.org/10.21236/ada591605.

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Panneton, Brian, and James Adametz. High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing (HPC) Environment: HPC Data Reduction Framework. Fort Belvoir, VA: Defense Technical Information Center, August 2015. http://dx.doi.org/10.21236/ada626053.

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Kiefer, Jack C., and J. S. Willett. Analysis of Nonresidential Content Value and Depth-Damage Data for Flood Damage Reduction Studies. Fort Belvoir, VA: Defense Technical Information Center, May 1996. http://dx.doi.org/10.21236/ada319792.

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Hanratty, Timothy, and John Richardson. A Visual Analytic for High-Dimensional Data Exploitation: The Heterogeneous Data-Reduction Proximity Tool. Fort Belvoir, VA: Defense Technical Information Center, July 2013. http://dx.doi.org/10.21236/ada591619.

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Ladd, Stevens, and Olsen. PR-179-15212-R01 Methane Reduction Data Analysis for 2-Stroke Lean Burn Natural Gas Engines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), November 2016. http://dx.doi.org/10.55274/r0011013.

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Анотація:
Existing test data from the Engines and Energy Conversion Laboratory and other sources are analyzed to examine the impact of retrofit technologies and operating parameters on greenhouse gas emissions. CO2, CH4, and CO2e (equivalent CO2) emissions are the focus of the study. The data analysis is focused on 4 main categories, engine performance maps, retrofit technology, uncontrolled parameters, and NOx permit level. Examination of the impact of HP replacement is also performed.
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Warren, Richard. Evaluation of Apex Alpha with LabWare-LIMS Data Reduction for Alpha Analyses. Office of Scientific and Technical Information (OSTI), October 2023. http://dx.doi.org/10.2172/2204289.

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10

Christiansen, R. L., J. S. Kalbus, and S. M. Howarth. Evaluation of methods for measuring relative permeability of anhydride from the Salado Formation: Sensitivity analysis and data reduction. Office of Scientific and Technical Information (OSTI), May 1997. http://dx.doi.org/10.2172/481478.

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