Relevant bibliographies by topics / Data dynamics

Academic literature on the topic 'Data dynamics'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Data dynamics"

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Ma, Tieju. "Exploring Dynamics of PRODYs with International Trade Data." International Journal of Social Science and Humanity 6, no. 8 (August 2016): 600–603. http://dx.doi.org/10.7763/ijssh.2016.v6.717.

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Mei, Zhuanglin, and Toshiki Oguchi. "Network Structure Identification Based on Measured Output Data Using Koopman Operators." Mathematics 11, no. 1 (December 26, 2022): 89. http://dx.doi.org/10.3390/math11010089.

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This paper considers the identification problem of network structures of interconnected dynamical systems using measured output data. In particular, we propose an identification method based on the measured output data of each node in the network whose dynamic is unknown. The proposed identification method consists of three steps: we first consider the outputs of the nodes to be all the states of the dynamics of the nodes, and the unmeasurable hidden states to be dynamical inputs with unknown dynamics. In the second step, we define the dynamical inputs as new variables and identify the dynamics of the network system with measured output data using Koopman operators. Finally, we extract the network structure from the identified dynamics as the information transmitted via the network. We show that the identified coupling functions, which represent the network structures, are actually projections of the dynamical inputs onto the space spanned by some observable functions. Numerical examples illustrate the validity of the obtained results.
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Zyphur, Michael J., Manuel C. Voelkle, Louis Tay, Paul D. Allison, Kristopher J. Preacher, Zhen Zhang, Ellen L. Hamaker, et al. "From Data to Causes II: Comparing Approaches to Panel Data Analysis." Organizational Research Methods 23, no. 4 (May 24, 2019): 688–716. http://dx.doi.org/10.1177/1094428119847280.

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This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time.
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SCHMID, PETER J. "Dynamic mode decomposition of numerical and experimental data." Journal of Fluid Mechanics 656 (July 1, 2010): 5–28. http://dx.doi.org/10.1017/s0022112010001217.

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The description of coherent features of fluid flow is essential to our understanding of fluid-dynamical and transport processes. A method is introduced that is able to extract dynamic information from flow fields that are either generated by a (direct) numerical simulation or visualized/measured in a physical experiment. The extracted dynamic modes, which can be interpreted as a generalization of global stability modes, can be used to describe the underlying physical mechanisms captured in the data sequence or to project large-scale problems onto a dynamical system of significantly fewer degrees of freedom. The concentration on subdomains of the flow field where relevant dynamics is expected allows the dissection of a complex flow into regions of localized instability phenomena and further illustrates the flexibility of the method, as does the description of the dynamics within a spatial framework. Demonstrations of the method are presented consisting of a plane channel flow, flow over a two-dimensional cavity, wake flow behind a flexible membrane and a jet passing between two cylinders.
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Wang, Haiyan, Yi Lin, and Fu Xiao. "A Lightweight Data Integrity Verification with Data Dynamics for Mobile Edge Computing." Security and Communication Networks 2022 (March 4, 2022): 1–15. http://dx.doi.org/10.1155/2022/1870779.

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As a special scenario of mobile cloud computing, mobile edge computing can meet the requirements of low latency of data integrity verification and support of mobility in mobile scenarios. However, most existing data integrity verification methods have relatively large computational overhead and few considerations of data dynamic update. To address the above problems, we propose a lightweight data integrity verification method that can support data dynamics in mobile edge computing scenarios. The proposed method is based on an algebraic signature and data integrity verification framework, which ensures security and reduces the computational overhead to achieve the requirement of lightweight. On this basis, analysis and proof of the feasibility, security, and privacy are given. At the same time, in order to support the dynamic update of the data, an optimized strategy based on matrix index is designed with low overhead. In comparison with other baseline methods, simulation experiments show that our method is superior in terms of computational overhead and has good performance in supporting data dynamics.
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Sivak, A. B., D. N. Demidov, and P. A. Sivak. "DIFFUSION CHARACTERISTICS OF RADIATION DEFECTS IN IRON: MOLECULAR DYNAMICS DATA." Problems of Atomic Science and Technology, Ser. Thermonuclear Fusion 44, no. 2 (2021): 148–57. http://dx.doi.org/10.21517/0202-3822-2021-44-2-148-157.

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Abraham, Ralph H. "Dynamics from Communications Data." American Journal of Psychotherapy 46, no. 4 (October 1992): 581–82. http://dx.doi.org/10.1176/appi.psychotherapy.1992.46.4.581.

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Thacker, William Carlisle, and Robert Bryan Long. "Fitting dynamics to data." Journal of Geophysical Research 93, no. C2 (1988): 1227. http://dx.doi.org/10.1029/jc093ic02p01227.

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Wang, Yan, Ying Liu, and Chao Ling Li. "Sensitive Cloud Data Deduplication with Data Dynamics." Applied Mechanics and Materials 556-562 (May 2014): 6236–40. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6236.

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To solve the confliction of data encryption and deduplication, a hMAC-Dedup scheme based on homomorphic MAC is proposed. In the scheme, every file is encrypted by the block level encryption and a tag is generated from each encrypted block. In the PoW (Proofs of oWnership) protocol, homomorphic MAC is used to check whether the file to store is real, by operating on the file’s encrypted blocks and pre-computed tags. The hMAC-Dedup can avoid the security shortcomings brought by hash-as-a-proof and provide encryption protection. It is also extended to support data dynamics, which includes block modification, insertion and deletion.
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Ahn, T. Y., K. F. Eman, and S. M. Wu. "Cutting Dynamics Identification by Dynamic Data System (DDS) Modeling Approach." Journal of Engineering for Industry 107, no. 2 (May 1, 1985): 91–94. http://dx.doi.org/10.1115/1.3185988.

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The dynamics of the cutting process have been conventionally characterized in terms of the Dynamic Cutting Force Coefficients (DCFC) which represent its transfer characteristics at discrete frequencies. However, this approach fails to obtain the transfer function of the process in closed analytical form. Anticipating the stochastic nature of the cutting process and the double modulation principle, a two-input one-output multivariate system was postulated for the dynamic cutting process identification model. The Dynamic Data System (DDS) methodology was used to formulate and characterize the dynamic cutting process using Modified Autoregressive Moving Average Vector (MARMAV) models. Subsequently, transfer functions of the inner and outer modulation dynamics of the cutting processes were obtained from the identified models.
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Dissertations / Theses on the topic "Data dynamics"

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Bone, Jeffrey. "Instantaneous dynamics of functional data." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59491.

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Time dynamic systems can be used in many applications to data modeling. In the case of longitudinal data, the dynamics of the underlying differential equation can often be inferred under minimal assumptions via smoothing based procedures. This is in contrast to the common technique of assuming a prespecified differential equation, and estimating it's parameters. In many cases, one wants to learn the dynamics of a differential equation that incorporates more than just one stochastic process. In the following, we propose extensions to existing two-step smoothing methods that allow for the presence of additional functional data arising from a second stochastic process. We further introduce model comparison techniques to assess the hypothesis that there is a significant change in fit provided by this additional process. These techniques are applied to the instantaneous dynamics of mouse growth data and allow us to make comparisons between mice who have been assigned different genetic and physical conditions. Finally, to study the statistical properties of our proposed techniques, we carry out a simulation study based on the mouse growth data. Supplementary material : http://hdl.handle.net/2429/59574
Science, Faculty of
Statistics, Department of
Graduate
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Brown, Hannah Marie. "Data Driven Modeling of Dynamics." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1618835986278106.

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Ibeh, Neke. "Inferring Viral Dynamics from Sequence Data." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35317.

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One of the primary objectives of infectious disease research is uncovering the direct link that exists between viral population dynamics and molecular evolution. For RNA viruses in particular, evolution occurs at such a rapid pace that epidemiological processes become ingrained into gene sequences. Conceptually, this link is easy to make: as RNA viruses spread throughout a population, they evolve with each new host infection. However, developing a quantitative understanding of this connection is difficult. Thus, the emerging discipline of phylodynamics is centered on reconciling epidemiology and phylogenetics using genetic analysis. Here, we present two research studies that draw on phylodynamic principles in order to characterize the progression and evolution of the Ebola virus and the human immunodefficiency virus (HIV). In the first study, the interplay between selection and epistasis in the Ebola virus genome is elucidated through the ancestral reconstruction of a critical region in the Ebola virus glycoprotein. Hence, we provide a novel mechanistic account of the structural changes that led up to the 2014 Ebola virus outbreak. The second study applies an approximate Bayesian computation (ABC) approach to the inference of epidemiological parameters. First, we demonstrate the accuracy of this approach with simulated data. Then, we infer the dynamics of the Swiss HIV-1 epidemic, illustrating the applicability of this statistical method to the public health sector. Altogether, this thesis unravels some of the complex dynamics that shape epidemic progression, and provides potential avenues for facilitating viral surveillance efforts.
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Wang, Yi. "Integrating data mining and system dynamics." Thesis, Cardiff University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.606794.

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Marzi, Tommaso. "Dynamical models for pedestrian dynamics using data from pedestrian flow sensors." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21219/.

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The purpose of this thesis is to give a contribution in a wider project regarding the development of new tools for the governance of tourist flows in Venice. Because of the virus COVID-19, this topic has increased in interest, since it can be used both to look for possible solutions to make public places safer and to study the spread of the virus itself. Once the testing of the sensors that provide the data on mobility is carried out, a macroscopic approach to the pedestrian dynamics based on the Fundamental Diagram is proposed: scenarios with different geometries as streets, crossroads or bridges are compared, focusing in particular on representative parameters of the model. In the last part, a microscopic approach to pedestrian mobility is presented: a simulation model is calibrated on the basis of the available data, in order to define whether it can actually reproduce the behaviour of a crowd.
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Ruiter, Julia. "Practical Chaos: Using Dynamical Systems to Encrypt Audio and Visual Data." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/scripps_theses/1389.

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Although dynamical systems have a multitude of classical uses in physics and applied mathematics, new research in theoretical computer science shows that dynamical systems can also be used as a highly secure method of encrypting data. Properties of Lorenz and similar systems of equations yield chaotic outputs that are good at masking the underlying data both physically and mathematically. This paper aims to show how Lorenz systems may be used to encrypt text and image data, as well as provide a framework for how physical mechanisms may be built using these properties to transmit encrypted wave signals.
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Ross, Natalie. "The dynamics of point-vortex data assimilation." Connect to online resource, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3303865.

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Peng, Zhenmin. "Interactive visualization of computational fluid dynamics data." Thesis, Swansea University, 2011. https://cronfa.swan.ac.uk/Record/cronfa42757.

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This thesis describes a literature study and a practical research in the area of flow visualization, with special emphasis on the interactive visualization of Computational Fluid Dynamics (CFD) datasets. Given the four main categories of flow visualization methodology; direct, geometric, texture-based and feature-based flow visualization, the research focus of our thesis is on the direct, geometric and feature-based techniques. And the feature-based flow visualization is highlighted in this thesis. After we present an overview of the state-of-the-art of the recent developments in the flow visualization in higher spatial dimensions (2.5D, 3D and 4D), we propose a fast, simple, and interactive glyph placement algorithm for investigating and visualizing boundary flow data based on unstructured, adaptive resolution boundary meshes from CFD dataset. Afterward, we propose a novel, automatic mesh-driven vector field clustering algorithm which couples the properties of the vector field and resolution of underlying mesh into a unified distance measure for producing high-level, intuitive and suggestive visualization of large, unstructured, adaptive resolution boundary CFD meshes based vector fields. Next we present a novel application with multiple-coordinated views for interactive information-assisted visualization of multidimensional marine turbine CFD data. Information visualization techniques are combined with user interaction to exploit our cognitive ability for intuitive extraction of flow features from CFD datasets. Later, we discuss the design and implementation of each visualization technique used in our interactive flow visualization framework, such as glyphs, streamlines, parallel coordinate plots, etc. In this thesis, we focus on the interactive visualization of the real-world CFD datasets, and present a number of new methods or algorithms to address several related challenges in flow visualization. We strongly believe that the user interaction is a crucial part of an effective data analysis and visualization of large and complex datasets such as CFD datasets we use in this thesis. In order to demonstrate the use of the proposed techniques in this thesis, CFD domain experts reviews are also provided.
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Dunton, Alec. "Topological Data Analysis for Systems of Coupled Oscillators." Scholarship @ Claremont, 2016. http://scholarship.claremont.edu/hmc_theses/79.

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Coupled oscillators, such as groups of fireflies or clusters of neurons, are found throughout nature and are frequently modeled in the applied mathematics literature. Earlier work by Kuramoto, Strogatz, and others has led to a deep understanding of the emergent behavior of systems of such oscillators using traditional dynamical systems methods. In this project we outline the application of techniques from topological data analysis to understanding the dynamics of systems of coupled oscillators. This includes the examination of partitions, partial synchronization, and attractors. By looking for clustering in a data space consisting of the phase change of oscillators over a set of time delays we hope to reconstruct attractors and identify members of these clusters.
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Torn, Ryan. "Using ensemble data assimilation for predictability and dynamics /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/10037.

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Books on the topic "Data dynamics"

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Unit, Engineering Sciences Data. Engineering sciences data: dynamics. London: Engineering Sciences Data Unit, 1991.

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Inmon, William H. The dynamics of data base. London: Prentice-Hall International, 1986.

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Sengupta, Jati K. Dynamics of Data Envelopment Analysis. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8506-4.

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J, Bird Thomas, ed. The dynamics of data base. Englewood Cliffs, N.J: Prentice-Hall, 1986.

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K, Bose T. Computational fluid dynamics. New York: Wiley, 1988.

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Chung, T. J. Computational fluid dynamics. 2nd ed. Cambridge: Cambridge University Press, 2010.

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Hogendoorn, R. A. Data compression in computational fluid dynamics. Amsterdam: National Aerospace Laboratory, 1986.

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H, Bunzel, Jensen Peter 1959-, and Westergård-Nielsen Niels C, eds. Panel data and labour market dynamics. Amsterdam: North-Holland, 1993.

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Devaney, Robert L., Kit C. Chan, and P. B. Vinod Kumar, eds. Topological Dynamics and Topological Data Analysis. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0174-3.

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Urban dynamics. Waltham, Mass: Pegasus Communications, 1999.

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Book chapters on the topic "Data dynamics"

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Koop, Henk. "Data Collection and Data Storage." In Forest Dynamics, 19–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-75012-0_3.

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Berti-Équille, Laure, and Javier Borge-Holthoefer. "Misinformation Dynamics." In Veracity of Data, 75–102. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-031-01855-8_5.

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Callaert, Julie, Elisabeth Epping, Gero Federkeil, Ben Jongbloed, Frans Kaiser, and Robert Tijssen. "Data Collection." In Higher Education Dynamics, 125–33. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-3005-2_8.

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Yapa, Sanjaya. "Data Migration." In Customizing Dynamics 365, 279–307. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4379-4_9.

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Treiber, Martin, and Arne Kesting. "Cross-Sectional Data." In Traffic Flow Dynamics, 13–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32460-4_3.

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Fraser, Andrew M. "Chaotic Data and Model Building." In Information Dynamics, 125–30. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-2305-9_8.

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Rajendran, Karthikeyan, Assimakis Kattis, Alexander Holiday, Risi Kondor, and Ioannis G. Kevrekidis. "Data Mining When Each Data Point is a Network." In Patterns of Dynamics, 289–317. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64173-7_17.

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Haines, K. "Dynamics and Data Assimilation in Oceanography." In Data Assimilation, 1–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78939-7_1.

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Charlton-Perez, Andrew, William Lahoz, and Richard Swinbank. "General Concepts in Meteorology and Dynamics." In Data Assimilation, 325–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-74703-1_13.

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Critchley, Sarah. "The Common Data Service." In Dynamics 365 Essentials, 1–14. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5911-5_1.

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Conference papers on the topic "Data dynamics"

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Nikolayev, M. U., E. V. Nikolayeva, and A. A. Lyashkov. "Data measuring channels calibration procedure." In 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2016. http://dx.doi.org/10.1109/dynamics.2016.7819052.

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Burdinsky, I. N., I. V. Karabanov, and A. S. Mironov. "Hydroacoustic signals of AUV data measuring systems." In 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2016. http://dx.doi.org/10.1109/dynamics.2016.7818992.

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Potapov, V. I., A. S. Gritsay, and D. A. Tyunkov. "Spectral analysis of retrospective data on power consumption." In 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2016. http://dx.doi.org/10.1109/dynamics.2016.7819063.

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Kosharnaya, Yulia, Sergey Yanchenko, and Alexey Kulikov. "Specifics of Data Mining Facilities as Energy Consumers." In 2018 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2018. http://dx.doi.org/10.1109/dynamics.2018.8601462.

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Belim, S. V., D. M. Brechka, T. A. Gorbunova, I. V. Schmidt, and I. B. Larionov. "Data mining based on an archaeological geoinformation system ArGIS." In 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2016. http://dx.doi.org/10.1109/dynamics.2016.7818975.

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Klypin, Dmitry N., Ilya V. Potapov, Dmitry A. Titov, and Antonina A. Garaeva. "The base tasks in development of Biomedical Data Information System." In 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2016. http://dx.doi.org/10.1109/dynamics.2016.7819027.

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Trofimov, S., and O. Ponomareva. "Data formalization by algebra of sets in multi-dimension space." In 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2016. http://dx.doi.org/10.1109/dynamics.2016.7819099.

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Nikolayev, M. U., Sergey V. Biryukov, E. V. Nikolayeva, V. A. Larioshkin, I. A. Leskov, and A. V. Varvarskiy. "Calibration algorithm automation for data measuring channels of electrical power systems." In 2017 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2017. http://dx.doi.org/10.1109/dynamics.2017.8239489.

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Kushko, E. A., and N. Yu Parotkin. "The research of technologies for secure data communication in dynamic networks." In 2017 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2017. http://dx.doi.org/10.1109/dynamics.2017.8239474.

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Averchenko, A. P., and B. D. Zhenatov. "Hartley transform as alternative to fourier transform in digital data processing systems." In 2014 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2014. http://dx.doi.org/10.1109/dynamics.2014.7005633.

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Reports on the topic "Data dynamics"

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Farboodi, Maryam, Roxana Mihet, Thomas Philippon, and Laura Veldkamp. Big Data and Firm Dynamics. Cambridge, MA: National Bureau of Economic Research, January 2019. http://dx.doi.org/10.3386/w25515.

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Kress, Joel D., Lee A. Collins, Leonid Burakovsky, Stuart D. Herring, Christopher Ticknor, and Scott Crockett. Simulations as Data: Quantum Molecular Dynamics. Office of Scientific and Technical Information (OSTI), October 2012. http://dx.doi.org/10.2172/1052783.

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Slinn, Donald N. Swash Zone Dynamics: Modeling and Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada627522.

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Zhou, Ning, Zhenyu Huang, Da Meng, Stephen T. Elbert, Shaobu Wang, and Ruisheng Diao. Capturing Dynamics in the Power Grid: Formulation of Dynamic State Estimation through Data Assimilation. Office of Scientific and Technical Information (OSTI), March 2014. http://dx.doi.org/10.2172/1172467.

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Lafontaine, Francine, and Kathryn Shaw. The Dynamics of Franchise Contracting: Evidence from Panel Data. Cambridge, MA: National Bureau of Economic Research, May 1996. http://dx.doi.org/10.3386/w5585.

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Kress, Joel D., Lee A. Collins, Leonid Burakovsky, Stuart D. Herring, Christopher Ticknor, and Scott Crockett. L2 Milestone: Simulations as Data. Quantum Molecular Dynamics Contribution. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1051092.

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Bonhomme, Stéphane, Richard Blundell, and Manuel Arellano. Earnings and consumption dynamics: a nonlinear panel data framework. Institute for Fiscal Studies, September 2015. http://dx.doi.org/10.1920/wp.cem.2015.5315.

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Blundell, Richard, Stéphane Bonhomme, and Manuel Arellano. Earnings and consumption dynamics: a nonlinear panel data framework. Institute for Fiscal Studies, September 2015. http://dx.doi.org/10.1920/wp.ifs.2015.1524.

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Barth, Erling, Sari Pekkala Kerr, and Claudia Olivetti. The Dynamics of Gender Earnings Differentials: Evidence from Establishment Data. Cambridge, MA: National Bureau of Economic Research, May 2017. http://dx.doi.org/10.3386/w23381.

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Kostelich, E. J., and H. D. Armbruster. Final report on characterizing the dynamics of spatio-temporal data. Office of Scientific and Technical Information (OSTI), September 1998. http://dx.doi.org/10.2172/656621.

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