Relevant bibliographies by topics / Identification methods

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Identification methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Identification methods"

1

Podolchak, N., G. Kovalchuk, and O. Savchyn. "Development methods of conflicts identification and evaluation." Economics, Entrepreneuship, Management 2, no. 2 (2015): 21–26. http://dx.doi.org/10.23939/eem2015.02.021.

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ORYMOWSKA, Joanna, and Paulina SOBKOWICZ. "Hazard identification methods." Scientific Journal of Silesian University of Technology. Series Transport 95 (June 1, 2017): 145–58. http://dx.doi.org/10.20858/sjsutst.2017.95.14.

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Piotrowski, Andrzej. "Hob Identification Methods." Advances in Science and Technology Research Journal 12, no. 1 (2018): 150–61. http://dx.doi.org/10.12913/22998624/81801.

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El-Sheikh, Garnal. "Recursive Identification Methods." International Conference on Aerospace Sciences and Aviation Technology 7, ASAT CONFERENCE (1997): 1–9. http://dx.doi.org/10.21608/asat.1997.25437.

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Oxley, James, and Richard Saunders. "Methods of rabbit identification." Veterinary Record 177, no. 16 (2015): 424. http://dx.doi.org/10.1136/vr.h5622.

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McKelvey, Tomas. "Frequency domain identification methods." Circuits, Systems, and Signal Processing 21, no. 1 (2002): 39–55. http://dx.doi.org/10.1007/bf01211650.

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Oganesyants, Lev, Vladislav Semipyatniy, Aram Galstyan, Ramil Vafin, Sergey Khurshudyan, and Anastasia Ryabova. "Multi-criteria food products identification by fuzzy logic methods." Foods and Raw Materials 8, no. 1 (2020): 12–19. http://dx.doi.org/10.21603/2308-4057-2020-1-12-19.

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The paper deals with the theory of fuzzy sets as applied to food industry products. The fuzzy indicator function is shown as a criterion for determining the properties of the product. We compared the approach of fuzzy and probabilistic classifiers, their fundamental differences and areas of applicability. As an example, a linear fuzzy classifier of the product according to one-dimensional criterion was given and an algorithm for its origination as well as approximation is considered, the latter being sufficient for the food industry for the most common case with one truth interval where the in
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O'RINOV, NODIRBEK TOXIRJONOVICH, JAMOLIDIN KOMOLDINOVICH ABDURXMANOV, and MUSLIMBEK ULUG'BEK O'G'LI OLIMOV. "Security levels of current biometric identification." Euro Afro Studies International Journal, (EASIJ.COM), 3, no. 6 (2021): 103–18. https://doi.org/10.5281/zenodo.5048324.

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The problem of minimizing the possibility of obtaining unauthorized access to information is investigated. The concept of the authentication process and its types are considered. Described are static and dynamic biometric authentication methods based on the use of biometric characteristics such as fingerprint, iris, retina, hand geometry, face geometry, face thermogram, voice and handwriting. The advantages of dynamic methods of biometric authentication are revealed. Based on the results of the analysis of these methods, it was proposed to use facial expressions as a biometric characteristic.
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lu, Emel, Nazl men, mer Karata, Eda Kiri, and Dilek lek. "Identification methods in mass disasters." Novel Forensic Research 2, no. 1 (2023): 12. http://dx.doi.org/10.5455/nofor.2023.03.07.

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Different numbers of people are affected in mass disasters. Identification of the disaster victims is a very important issue. For an accurate identification process, good and fast management of this process is of great importance. Identification of the victims includes investigating the disaster field, autopsy and following that post mortem data collection, ante-mortem data collection, and comparison of collected data. If these steps can be applied properly, identification of victims will be completed successfully. There are different techniques for victim identification in mass disasters and
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Rathee, Dr Manu, and Dr Kusum Yadav. "Denture Identification Methods: A Review." IOSR Journal of Dental and Medical Sciences 13, no. 10 (2014): 58–61. http://dx.doi.org/10.9790/0853-131055861.

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Dissertations / Theses on the topic "Identification methods"

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Lyzell, Christian. "Initialization Methods for System Identification." Licentiate thesis, Linköping University, Linköping University, Automatic Control, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-51688.

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<p>In the system identification community a popular framework for the problem of estimating a parametrized model structure given a sequence of input and output pairs is given by the prediction-error method. This method tries to find the parameters which maximize the prediction capability of the corresponding model via the minimization of some chosen cost function that depends on the prediction error. This optimization problem is often quite complex with several local minima and is commonly solved using a local search algorithm. Thus, it is important to find a good initial estimate for the loca
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Kukreja, Sunil L. "Parametric methods for nonlinear system identification." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=37752.

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In this thesis, we have developed practical methods for the identification of linear, nonlinear and hybrid (multimode) systems which are applicable under relatively general conditions, i.e., when assumptions and conditions of the estimation technique are not violated. Since these algorithms were not designed specifically with any system(s) in mind, they should be applicable to experiments on a variety of systems in many different disciplines.<br>Results demonstrate that the (polynomial) NARMAX (Nonlinear Autoregressive, Moving Average eXogenous) model class is useful for modeling the input-out
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Dautbegovic, Dino. "Convex Optimization Methods for System Identification." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-34808.

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The extensive use of a least-squares problem formulation in many fields is partly motivated by the existence of an analytic solution formula which makes the theory comprehensible and readily applicable, but also easily embedded in computer-aided design or analysis tools. While the mathematics behind convex optimization has been studied for about a century, several recent researches have stimulated a new interest in the topic. Convex optimization, being a special class of mathematical optimization problems, can be considered as generalization of both least-squares and linear programming. As in
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Shpitser, Ilya. "Complete identification methods for causal inference." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1708387761&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Dadgar, Ashraf. "Methods for identification and diagnosis of amyloidosis." Thesis, Uppsala University, Department of Medical Biochemistry and Microbiology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7175.

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<p>The amyloidoses are biochemically heterogeneous diseases with patholophysiologic deposits of various proteins. Amyloid deposits can occur either localized to one organ or tissue or as part of a systemic disease with deposits in many different tissue. The clinical course, prognosis and therapy are different for each type of amyloidosis and therefore a type specific diagnosis is demanded as early as possible. We describe a method for typing of the most common systemic amyloidoses based on Western blot analysis combined with specific</p><p>in- house antibodies, using subcutaneous fat biopsies.
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Shi, Ruijie. "Subspace identification methods for process dynamic modeling /." *McMaster only, 2001.

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Hwang, Ho-yon. "Identification methods for physical parameters in structures." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/15652.

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Wan, Yufeng. "Application of kernal methods to system identification." Thesis, University of Sheffield, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427224.

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Brooks, Christopher Allen 1978. "Terrain identification methods for planetary exploration rovers." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/30303.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004.<br>Includes bibliographical references (leaves 77-82).<br>Autonomous mobility in rough terrain is becoming increasingly important for planetary exploration rovers. Increased knowledge of local terrain properties is critical to ensure a rover's safety, especially when driving on slopes or rough surfaces. This thesis presents two methods for using on-board sensors to identify local terrain conditions. The first method visually measures sinkage of a rover wheel into deformable terrain, based on a single c
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Manukyan, Narine. "Improved Methods for Cluster Identification and Visualization." ScholarWorks @ UVM, 2011. http://scholarworks.uvm.edu/graddis/147.

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Self-organizing maps (SOMs) are self-organized projections of high dimensional data onto a low, typically two dimensional (2D), map wherein vector similarity is implicitly translated into topological closeness in the 2D projection. They are thus used for clustering and visualization of high dimensional data. However it is often challenging to interpret the results due to drawbacks of currently used methods for identifying and visualizing cluster boundaries in the resulting feature maps. In this thesis we introduce a new phase to the SOM that we refer to as the Cluster Reinforcement (CR)
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Books on the topic "Identification methods"

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Crawley, Frank. Hazard identification methods. Institution of Chemical Engineers, 2003.

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Katayama, Tohru. Subspace Methods for System Identification. Springer London, 2005. http://dx.doi.org/10.1007/1-84628-158-x.

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Braun, Dietrich. Simple Methods for Identification of Plastics. Carl Hanser Verlag GmbH & Co. KG, 2013. http://dx.doi.org/10.3139/9781569905425.

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Chatzi, Eleni, and Costas Papadimitriou, eds. Identification Methods for Structural Health Monitoring. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32077-9.

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United States. Animal and Plant Health Inspection Service. Veterinary Services. Centers for Epidemiology and Animal Health. and National Animal Health Monitoring System (U.S.), eds. Unique identification methods for U.S. equids. U.S. Dept. of Agriculture, APHIS, 1999.

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Braun, Dietrich. Simple Methods for Identification of Plastics. Carl Hanser Verlag GmbH & Co. KG, 2013. http://dx.doi.org/10.1007/978-1-56990-542-5.

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C, Armanino, ed. Chemometrics and species identification. Springer-Verlag, 1987.

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Mount, Donald Irvin. Methods for aquatic toxicity identification evaluations: Phase II toxicity identification procedures. U.S. Environmental Protection Agency, Environmental Research Laboratory, National Effluent Toxicity Assessment Center, 1989.

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Söderström, Torsten. Errors-in-Variables Methods in System Identification. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75001-9.

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Sol, H., and C. W. J. Oomens, eds. Material Identification Using Mixed Numerical Experimental Methods. Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-009-1471-1.

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Book chapters on the topic "Identification methods"

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Wagner, Scott A. "Identification Methods." In Death Investigation, 2nd ed. CRC Press, 2023. http://dx.doi.org/10.4324/9780429318665-8.

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Choi, ByoungSeon. "Pattern Identification Methods." In ARMA Model Identification. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4613-9745-8_5.

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Choi, ByoungSeon. "The Autocorrelation Methods." In ARMA Model Identification. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4613-9745-8_2.

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Choi, ByoungSeon. "Penalty Function Methods." In ARMA Model Identification. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4613-9745-8_3.

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Choi, ByoungSeon. "Innovation Regression Methods." In ARMA Model Identification. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4613-9745-8_4.

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Choi, ByoungSeon. "Testing Hypothesis Methods." In ARMA Model Identification. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4613-9745-8_6.

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Isermann, Rolf, and Marco Münchhof. "Subspace Methods." In Identification of Dynamic Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-78879-9_16.

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Nelles, Oliver. "Applications of Advanced Methods." In Nonlinear System Identification. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04323-3_22.

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Nelles, Oliver. "Applications of Advanced Methods." In Nonlinear System Identification. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_28.

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Mahmoud, Magdi S., and Yuanqing Xia. "System Identification Methods." In Applied Control Systems Design. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2879-3_3.

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Conference papers on the topic "Identification methods"

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Mira, Fahad. "Malware Detection and Identification Methods." In 2024 International Conference on Computer and Applications (ICCA). IEEE, 2024. https://doi.org/10.1109/icca62237.2024.10928153.

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Muzny, Grace, and Luke Zettlemoyer. "Automatic Idiom Identification in Wiktionary." In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2013. http://dx.doi.org/10.18653/v1/d13-1145.

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Shuaitao, Ren, Song Lei, and Zhou Hua. "Research on Blockchain-based Mahogany Identification Methods." In 2024 7th International Conference on Electronics Technology (ICET). IEEE, 2024. http://dx.doi.org/10.1109/icet61945.2024.10673350.

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Shrene Shifna, N. Fathima, and K. Baalaji. "Precision Farming: Advanced Methods for Weed Identification." In 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON). IEEE, 2024. https://doi.org/10.1109/delcon64804.2024.10866880.

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Johnston, J. M., Lawrence J. Myers, L. Paul Waggoner, and Marc Williams. "Determination of canine olfactory thesholds using operant laboratory methods." In Substance Identification Technologies, edited by Geoffrey L. Harding, Richard C. Lanza, Lawrence J. Myers, and Peter A. Young. SPIE, 1994. http://dx.doi.org/10.1117/12.171244.

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Jiang, Ching-Fen, and Jia-Yin Li. "Comparison and evaluation of multimodality brain image registration methods." In Modelling, Simulation and Identification. ACTAPRESS, 2018. http://dx.doi.org/10.2316/p.2018.857-024.

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Jain, V. K., G. Kaur, and A. S. Arora. "Comparison of Methods for Electromyography Diagnosis using Time Domain Features." In Modelling, Identification, and Control. ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.702-065.

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Elbkosh, Abdulmajed O. "Modeling of Switching DC-DC Regulators: Review of Methods and Applications." In Modelling, Identification and Control. ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.799-080.

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Sulitka, M., and J. Zavřel. "Comparison of Several Methods for Reduction of Machine Tool FEM Models." In Modelling, Identification, and Control. ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.675-072.

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Strandlie, A. "Filtering, robust and adaptive methods for track reconstruction." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628556.

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Reports on the topic "Identification methods"

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Robinson, Judith, Robert Mackley, Mark Rockhold, Timothy Johnson, and Piyoosh Jaysaval. Geophysical Methods for Stratigraphic Identification. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1684643.

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Robinson, Judith, Robert Mackley, Mark Rockhold, et al. Geophysical Methods for Stratigraphic Identification. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1810384.

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Graham, Bryan. Methods of Identification in Social Networks. National Bureau of Economic Research, 2014. http://dx.doi.org/10.3386/w20414.

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Va'Vra, J. Particle identification methods in High Energy Physics. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/753277.

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Klyde, David, Chuck Harris, Peter M. Thompson, and Edward N. Bachelder. System Identification Methods for Improving Flutter Flight Test Techniques. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada426452.

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Burns, John A., Eugene M. Cliff, and Lizette Zietsman. Computational Methods for Identification, Optimization and Control of PDE Systems. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada523367.

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Butler, Dwain K., Ernesto R. Cespedes, Cary B. Cox, and Paul J. Wolfe. Multisensor Methods for Buried Unexploded Ordnance Deteciton, Discrimination, and Identification. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada354124.

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Giacomini, Raffaella, Toru Kitagawa, and Matthew Read. Identification and Inference under Narrative Restrictions. Reserve Bank of Australia, 2023. http://dx.doi.org/10.47688/rdp2023-07.

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We consider structural vector autoregressions subject to narrative restrictions, which are inequalities involving structural shocks in specific time periods (e.g. shock signs in given quarters). Narrative restrictions are used widely in the empirical literature. However, under these restrictions, there are no formal results on identification or the properties of frequentist approaches to inference, and existing Bayesian methods can be sensitive to prior choice. We provide formal results on identification, propose a computationally tractable robust Bayesian method that eliminates prior sensitiv
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Rand, Robert S., John M. Grossmann, Roger N. Clark, Eric Livo, and Thomas Parr. Background Suppression and Feature Based Spectroscopy Methods for Subpixel Material Identification. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada570048.

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Pozzi, S., and F. J. Segovia. Application of stochastic and artificial intelligence methods for nuclear material identification. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/750980.

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