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Artykuły w czasopismach na temat "System identification- Geophysical signals"
Lubis, Muhammad Zainuddin, Kasih Anggraini, Husnul Kausarian i Sri Pujiyati. "Review: Marine Seismic And Side-Scan Sonar Investigations For Seabed Identification With Sonar System". Journal of Geoscience, Engineering, Environment, and Technology 2, nr 2 (1.06.2017): 166. http://dx.doi.org/10.24273/jgeet.2017.2.2.253.
Pełny tekst źródłaAbramovych, А. О. "IMPROVING THE EDDY CURRENT IDENTIFIER OF METALS BASED ON THE CORRELATION APPROACH". Radio Electronics, Computer Science, Control, nr 4 (3.12.2022): 7. http://dx.doi.org/10.15588/1607-3274-2022-4-1.
Pełny tekst źródłaAbazarsa, Fariba, Fariborz Nateghi, S. Farid Ghahari i Ertugrul Taciroglu. "Blind Modal Identification of Non-Classically Damped Systems from Free or Ambient Vibration Records". Earthquake Spectra 29, nr 4 (listopad 2013): 1137–57. http://dx.doi.org/10.1193/031712eqs093m.
Pełny tekst źródłaBodin, Jacques, Gilles Porel, Benoît Nauleau i Denis Paquet. "Delineation of discrete conduit networks in karst aquifers via combined analysis of tracer tests and geophysical data". Hydrology and Earth System Sciences 26, nr 6 (1.04.2022): 1713–26. http://dx.doi.org/10.5194/hess-26-1713-2022.
Pełny tekst źródłaAkhi, A. V. "Akhi A.V. Efficiency of Identification of Complex Noise-Like Signal Classes by Dolphins (<i>Tursiops truncatus</i>) under Simultaneous Presentation Spatial Uncertainty". Fundamental and Applied Hydrophysics 16, nr 1 (23.04.2023): 90–97. http://dx.doi.org/10.59887/fpg/vxe3-6531-nkup.
Pełny tekst źródłaKOH, C. G., i M. J. PERRY. "STRUCTURAL DAMAGE QUANTIFICATION BY SYSTEM IDENTIFICATION". Journal of Earthquake and Tsunami 01, nr 03 (wrzesień 2007): 211–31. http://dx.doi.org/10.1142/s1793431107000134.
Pełny tekst źródłaZamora Santacruz, Mario Fernando, Mario RUIZ i Jose OSORNO. "The Exploration of Hydrocarbons and Mining & Energy Resources Using Non-Seismic Methods - Nuclear Magnetic Resonance Technology". Technium: Romanian Journal of Applied Sciences and Technology 2, nr 7 (22.12.2020): 372–89. http://dx.doi.org/10.47577/technium.v2i7.2258.
Pełny tekst źródłaMunoz-Martin, Joan Francesc, Raul Onrubia, Daniel Pascual, Hyuk Park, Adriano Camps, Christoph Rüdiger, Jeffrey Walker i Alessandra Monerris. "Untangling the Incoherent and Coherent Scattering Components in GNSS-R and Novel Applications". Remote Sensing 12, nr 7 (9.04.2020): 1208. http://dx.doi.org/10.3390/rs12071208.
Pełny tekst źródłaKaczmarek, Adrian, i Bernard Kontny. "Identification of the Noise Model in the Time Series of GNSS Stations Coordinates Using Wavelet Analysis". Remote Sensing 10, nr 10 (10.10.2018): 1611. http://dx.doi.org/10.3390/rs10101611.
Pełny tekst źródłaSteinitz, G., P. Kotlarsky i O. Piatibratova. "Indications for influence of artificial (man-made) activity on radon signals, in simulation experiments". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 472, nr 2195 (listopad 2016): 20160311. http://dx.doi.org/10.1098/rspa.2016.0311.
Pełny tekst źródłaRozprawy doktorskie na temat "System identification- Geophysical signals"
AlHilal, M. H. "System identification using pseudorandom signals". Thesis, Swansea University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635747.
Pełny tekst źródłaSolomou, Michael. "System identification in the presence of nonlinear distortions using multisine signals". Thesis, University of South Wales, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289160.
Pełny tekst źródłaDeng, Qingwei 1968. "Identification of dendritic targeting signals of voltage-gated potassium channel 3". Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82219.
Pełny tekst źródłaPIOLDI, Fabio. "Time and Frequency Domain output-only system identification from earthquake-induced structural response signals". Doctoral thesis, Università degli studi di Bergamo, 2017. http://hdl.handle.net/10446/77137.
Pełny tekst źródłaSpeckhahn, Marcus M. "Identification of acoustically active Arctic pressure ridges through the use of RADARSAT Geophysical Processor System (RGPS) sea ice products". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1998. http://handle.dtic.mil/100.2/ADA351618.
Pełny tekst źródła"June 1998." Thesis advisor(s): Robert H. Bourke, James H. Wilson. Includes bibliographical references (p. 301-304). Also available online.
Tuffner, Francis K. "Computationally efficient weighted updating of statistical parameter estimates for time varying signals with application to power system identification". Laramie, Wyo. : University of Wyoming, 2008. http://proquest.umi.com/pqdweb?did=1674094221&sid=4&Fmt=2&clientId=18949&RQT=309&VName=PQD.
Pełny tekst źródłaKuramoto, André Seichi Ribeiro. "Projeto de sinais de excitação para identificação multivariável de plantas industriais". Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-07112016-144658/.
Pełny tekst źródłaIn this work methods for generating sets of excitation signals for system identification are discussed and evaluated. This study is focused on applications in the process industries, particularly in oil refining. The operational constraints of the oil refining industry are becoming increasingly severe due to increased energy demand, quality of products, oil price variations,market competition and other economic, environmental and energy efficiency factors. In this scenario the use of model predictive control techniques is increasing, thus the demand for plant identification as well. The particular characteristics of the processing plants impose restrictions to the project and application of excitation signals. Various methods for generating signals accessible in the literature and three new others proposed in this work are compared with reference to these restrictions. One of the main constraints for applying excitation signals for identification is relative to the period available for excitation of the plant. Thus, for the proper use of this time interval, it is necessary to ensure the success of an experiment prior to its implementation. In the literature there are several performance measurements for evaluation of sets of excitation signals prior to the experiment. This work proposes two new measures to complement the evaluation. The effectiveness of the generating methods and performance measurements for excitation signals is evaluated by simulation of multivariable identification of two typical oil refining plants. The conclusions of this work briefly present these evaluations, as well as some suggestions of future work for the continuity of the current research.
LIMA, Rafael Bezerra Correia. "Metodologia para identificação de sistemas em espaço de estados por meio de excitações pulsadas". Universidade Federal de Campina Grande, 2016. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1287.
Pełny tekst źródłaMade available in DSpace on 2018-07-30T14:13:06Z (GMT). No. of bitstreams: 1 RAFAEL BEZERRA CORREIA LIMA - TESE PPGEE 2016..pdf: 2324960 bytes, checksum: db1b63193864e8e19bcba191952df2b9 (MD5) Previous issue date: 2016-09-20
Nesse trabalho são apresentadas contribuições na área de identificação de sistemas representados em espaço de estados. E proposta uma metodologia completa para estimação de modelos que representem as principais dinâmicas de processos industriais. O fluxo natural das procedimentos de identificação consiste da coleta experimental dos dados, seguido pela escolha dos modelos candidatos e da utilização de um critério de ajuste que selecione o melhor modelo possível. Nesse sentido é proposta uma metodologia para estimativa de modelos em espaço de estados, utilizando excitações pulsadas. A abordagem desenvolvida combina algoritmos precisos e eficientes com experimentos rápidos, adequados a ambientes industriais. O projeto das excitações é realizado em tempo real, por meio de informações coletadas em um curto experimento inicial, baseado em uma única oscilação de uma estrutura realimentada por um relê. Esse mecanismo possibilita uma estimativa preliminar do atraso e da constante de tempo dominante do sistema. O método de identificação proposto é baseado na teoria de realizações de Kalman. É apresentada uma reformulação do problema de realizações clássico, para comportar sinais de entrada pulsados. Essa abordagem se mostra computacionalmente eficiente, assim como apresenta resultados semelhantes aos métodos de benehmark. A técnica possibilita também a estimativa de atrasos de transporte e a inserção de conhecimentos prévios por meio de um problema de otimização com restrições via LMI Linear Matrix Incqualities. Em muitos casos, somente as características principais do sistema são relevantes em um projeto de sistema de controle. Portanto é proposta uma técnica para obtenção de modelos de primeira ordem com atraso, a partir da redução de modelos balanceados em espaço de estados. Por fim, todas as contribuições discutidas nesse trabalho de tese são validadas em uma série de plantas experimentais em escala de laboratório. Plantas essas, projetadas e construídas com o intuito de emular o cotidiano operacional de instalações industriais reais.
This work Íntroduces contributions related to thc field of systems identification of state space models. It is proposed a complete methodology for modei estimation that encompasses the main dynamics of industrial processes. The natural flxix of the identification procedures rests on the the empirical collection of data followed by the choice of candidate modela and posterior use ot an adjusting criteria that drafts the best model amoug the contenders. In this sense. a uew methodology is proposed for models estimation in state spaces using pulsed excitation signal. The developed approach combines accurate and efhcient algorithms with quick experíments whose are suitable for the industrial environment. The excitatiou design is performed in real time by means of information collected in a snort mitíai experíment based in an single oscillation of a relay feedback. This mechanism allows a preliminary estimation of both delay and time constant prevalent in the system. The identification method proposed is based on Kalmairs realization theory. The thesis íntroduces a reformulation of the classic realization problem so it can admit pulsed input signals. This approaíth show itself as computationally efficient as well as provides similar results eompared to those obtained when perfonning the benchmark methods. Moreover, the technic allows the transport delay estimation and insertion of prior knowledge by means of an optimization problem with restrictions via linear matrix inequalities restrictions. In many cases only the characteristics of the main system are relevant in control systems design. Therefore a technique for the attainment first order models with time delay based on balanced state space models reduction. Lastly ali the contributions provided aíong the thesis are discussed and validated in a series of pilot scale plants. designed and built to emulate the operational cycle in real industrial plants.
Kasaei, Shohreh. "Fingerprint analysis using wavelet transform with application to compression and feature extraction". Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36053/7/36053_Digitised_Thesis.pdf.
Pełny tekst źródłaKarthikeyan, E. "Studies of system identification and denoising for geophysical signals". Thesis, 2018. http://eprint.iitd.ac.in:80//handle/2074/7928.
Pełny tekst źródłaKsiążki na temat "System identification- Geophysical signals"
Keith, Godfrey, red. Perturbation signals for system identification. New York: Prentice Hall, 1993.
Znajdź pełny tekst źródłaCoca, D. Smoothing chaotic signals for system identification: A multiresolution wavelet decomposition approach. Sheffield: University of Sheffield, Department of Automatic Control and Systems Engineering, 1995.
Znajdź pełny tekst źródłaProgri, Ilir. Geolocation of RF signals: Principles and simulations. New York: Springer, 2011.
Znajdź pełny tekst źródłaSpeckhahn, Marcus M. Identification of acoustically active Arctic pressure ridges through the use of RADARSAT Geophysical Processor System (RGPS) sea ice products. Monterey, Calif: Naval Postgraduate School, 1998.
Znajdź pełny tekst źródłaOgunfunmi, Tokunbo. Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches (Signals and Communication Technology). Springer, 2007.
Znajdź pełny tekst źródłaAdaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches (Signals and Communication Technology). Springer, 2007.
Znajdź pełny tekst źródłaProgri, Ilir. Geolocation of RF Signals: Principles and Simulations. Springer, 2011.
Znajdź pełny tekst źródłaProgri, Ilir. Geolocation of RF Signals: Principles and Simulations. Springer, 2014.
Znajdź pełny tekst źródłaProgri, Ilir. Geolocation of RF Signals: Principles and Simulations. Springer, 2011.
Znajdź pełny tekst źródłaIdentification of Acoustically Active Arctic Pressure Ridges Through theUse of RADARSAT Geophysical Processor System (RGPS) Sea Ice Products. Storming Media, 1998.
Znajdź pełny tekst źródłaCzęści książek na temat "System identification- Geophysical signals"
Haber, Robert, i László Keviczky. "Test Signals for Identification". W Nonlinear System Identification — Input-Output Modeling Approach, 119–98. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4481-0_2.
Pełny tekst źródłaTan, Ai Hui, i Keith Richard Godfrey. "Design of Pseudorandom Signals for Linear System Identification". W Industrial Process Identification, 25–58. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-03661-4_2.
Pełny tekst źródłaTan, Ai Hui, i Keith Richard Godfrey. "Design of Computer-Optimised Signals for Linear System Identification". W Industrial Process Identification, 59–94. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-03661-4_3.
Pełny tekst źródłaLingener, A. "Determination of Frequency Responses by Means of Pseudorandom Signals". W Application of System Identification in Engineering, 483–97. Vienna: Springer Vienna, 1988. http://dx.doi.org/10.1007/978-3-7091-2628-8_13.
Pełny tekst źródłaPillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao i Lennart Ljung. "Classical System Identification". W Regularized System Identification, 17–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_2.
Pełny tekst źródłaDe Moor, Bart, Peter Van Overschee i Wouter Favoreel. "Algorithms for Subspace State-Space System Identification: An Overview". W Applied and Computational Control, Signals, and Circuits, 247–311. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-0571-5_6.
Pełny tekst źródłaMarkovsky, Ivan, Anton Amann i Sabine Van Huffel. "Application of Filtering Methods for Removal of Resuscitation Artifacts from Human ECG Signals". W System Identification, Environmental Modelling, and Control System Design, 273–91. London: Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-974-1_14.
Pełny tekst źródłaBo, L. I. U., Z. H. A. O. Jun i Q. I. A. N. Jixin. "Design and Analysis of Test Signals for System Identification". W Computational Science – ICCS 2006, 593–600. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758532_78.
Pełny tekst źródłaWestwick, David T., i Robert E. Kearney. "Identification of Multiple-Input Nonlinear Systems Using Non-White Test Signals". W Advanced Methods of Physiological System Modeling, 163–78. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4757-9024-5_8.
Pełny tekst źródłaFaghih, Rose T. "From Physiological Signals to Pulsatile Dynamics: A Sparse System Identification Approach". W Dynamic Neuroscience, 239–65. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71976-4_10.
Pełny tekst źródłaStreszczenia konferencji na temat "System identification- Geophysical signals"
Palma, Gabriel, Ana Aquino, Patricia Monticelli, Luciano Verdade, Charles Markham i Rafael Moral. "A machine vision system for avian song classification with CNN’s". W 24th Irish Machine Vision and Image Processing Conference. Irish Pattern Recognition and Classification Society, 2022. http://dx.doi.org/10.56541/mhzn4111.
Pełny tekst źródłaTristanov, Alexander, Olga Lukovenkova, Yuri Marapulets i Alina Kim. "Improvement of methods for sparse model identification of pulsed geophysical signals". W 2019 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). IEEE, 2019. http://dx.doi.org/10.23919/spa.2019.8936817.
Pełny tekst źródłaTsumura, K., i J. Maciejowski. "Optimal quantization of signals for system identification". W 2003 European Control Conference (ECC). IEEE, 2003. http://dx.doi.org/10.23919/ecc.2003.7085053.
Pełny tekst źródła"Identification of sediment-related disaster based on seismic and acoustic signals ("MM-Identification")". W Earth System Sciences (ESS). Vienna: Austrian Academy of Sciences Press, 2018. http://dx.doi.org/10.1553/ess-mmidentifications1.
Pełny tekst źródłaNaik, Manjish, i Douglas Cochran. "Nonlinear system identification using compressed sensing". W 2012 46th Asilomar Conference on Signals, Systems and Computers. IEEE, 2012. http://dx.doi.org/10.1109/acssc.2012.6489039.
Pełny tekst źródłaBhat, Harish S. "System Identification via the Adjoint Method". W 2021 55th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2021. http://dx.doi.org/10.1109/ieeeconf53345.2021.9723391.
Pełny tekst źródłaMatta, Rafik, Johnny K. H. Lau, Foteini Agrafioti i Dimitrios Hatzinakos. "Real-time continuous identification system using ECG signals". W 2011 24th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2011. http://dx.doi.org/10.1109/ccece.2011.6030676.
Pełny tekst źródłaBingham, Jeffrey T., i Marco P. Schoen. "Characterization of Myoelectric Signals Using System Identification Techniques". W ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59904.
Pełny tekst źródłaPapi, F., D. Tarchi, M. Vespe, F. Oliveri i G. Aulicino. "Radiolocation and tracking of automatic identification system signals". W 2014 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2014. http://dx.doi.org/10.1109/ssp.2014.6884686.
Pełny tekst źródłaMurphy, K. "System identification and modelling of a milk pasteurisation plant". W Irish Signals and Systems Conference 2004. IEE, 2004. http://dx.doi.org/10.1049/cp:20040592.
Pełny tekst źródłaRaporty organizacyjne na temat "System identification- Geophysical signals"
Corriveau, L., J. F. Montreuil, O. Blein, E. Potter, M. Ansari, J. Craven, R. Enkin i in. Metasomatic iron and alkali calcic (MIAC) system frameworks: a TGI-6 task force to help de-risk exploration for IOCG, IOA and affiliated primary critical metal deposits. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329093.
Pełny tekst źródłaPhillips, Donald, i Yoram Kapulnik. Using Flavonoids to Control in vitro Development of Vesicular Arbuscular Mycorrhizal Fungi. United States Department of Agriculture, styczeń 1995. http://dx.doi.org/10.32747/1995.7613012.bard.
Pełny tekst źródłaLers, Amnon, i Gan Susheng. Study of the regulatory mechanism involved in dark-induced Postharvest leaf senescence. United States Department of Agriculture, styczeń 2009. http://dx.doi.org/10.32747/2009.7591734.bard.
Pełny tekst źródłaWagner, D. Ry, Eliezer Lifschitz i Steve A. Kay. Molecular Genetic Analysis of Flowering in Arabidopsis and Tomato. United States Department of Agriculture, maj 2002. http://dx.doi.org/10.32747/2002.7585198.bard.
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