Academic literature on the topic 'Kautz'
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Journal articles on the topic "Kautz"
Wahlberg, Bo. "Laguerre and Kautz Models." IFAC Proceedings Volumes 27, no. 8 (July 1994): 965–76. http://dx.doi.org/10.1016/s1474-6670(17)47834-7.
Full textBermond, J. C., N. Homobono, and C. Peyrat. "Connectivity of kautz networks." Discrete Mathematics 114, no. 1-3 (April 1993): 51–62. http://dx.doi.org/10.1016/0012-365x(93)90355-w.
Full textBöhmová, Katerina, Cristina Dalfó, and Clemens Huemer. "The diameter of cyclic Kautz digraphs." Filomat 31, no. 20 (2017): 6551–60. http://dx.doi.org/10.2298/fil1720551b.
Full textWahlberg, B. "System identification using Kautz models." IEEE Transactions on Automatic Control 39, no. 6 (June 1994): 1276–82. http://dx.doi.org/10.1109/9.293196.
Full textMorvan, R., N. Tanguy, P. Vilbé, and L. C. Calvez. "Pertinent parameters for Kautz approximation." Electronics Letters 36, no. 8 (2000): 769. http://dx.doi.org/10.1049/el:20000581.
Full textXu, Jun-Ming, Ye-Zhou Wu, Jia Huang, and Chao Yang. "Feedback numbers of Kautz digraphs." Discrete Mathematics 307, no. 13 (June 2007): 1589–99. http://dx.doi.org/10.1016/j.disc.2006.09.010.
Full textEttefagh, Massoud Hemmasian, José De Doná, Mahyar Naraghi, and Farzad Towhidkhah. "Control of Constrained Linear-Time Varying Systems via Kautz Parametrization of Model Predictive Control Scheme." Emerging Science Journal 1, no. 2 (September 19, 2017): 65. http://dx.doi.org/10.28991/esj-2017-01117.
Full textBahar, Arash, Ali Chaibakhsh, and Sajad Haqdadi. "Identification of MR Damper Based on Normalized Bouc-Wen Model Using Neural Network." Applied Mechanics and Materials 229-231 (November 2012): 2140–44. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.2140.
Full textRolim, José, Pavel Tvrdik, Jan Trdlička, and Imrich Vrto. "Bisecting de Bruijn and Kautz graphs." Discrete Applied Mathematics 85, no. 1 (June 1998): 87–97. http://dx.doi.org/10.1016/s0166-218x(98)00031-6.
Full textBöhmová, K., C. Dalfó, and C. Huemer. "The Diameter of Cyclic Kautz Digraphs." Electronic Notes in Discrete Mathematics 49 (November 2015): 323–30. http://dx.doi.org/10.1016/j.endm.2015.06.044.
Full textDissertations / Theses on the topic "Kautz"
Kautz, Thomas [Verfasser]. "Impulsmagnetisches Beschneiden von dünnwandigen Hohlprofilen / Thomas Kautz." Aachen : Shaker, 2008. http://d-nb.info/1164341820/34.
Full textRosa, Alex da. "Desenvolvimento de modelos discretos de Volterra usando funções de Kautz." [s.n.], 2005. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260139.
Full textDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: Este trabalho analisa a modelagem de sistemas nao-lineares utilizando modelos de Wiener/Volterra com funcoes ortonormais de Kautz. Os modelos de Volterra sao uma generalizacao do modelo resposta ao impulso para a descricao de sistemas naolineares. Esses modelos necessitam de um numero consideravel de termos para a representacao dos kernels de Volterra. Essa complexidade pode ser reduzida utilizando-se uma representacao do tipo Wiener/Volterra, em que os kernels sao desenvolvidos utilizando uma base de funcoes ortonormais. Sao discutidos aspectos da selecao dos parametros livres (polos) que caracterizam essas funcoes, particularmente a selecao otima dos polos complexos das funcoes de Kautz. Este problema e resolvido minimizando-se o limitante superior do erro que surge a partir da aproximação truncada dos kernels de Volterra usando-se as funcoes de Kautz. Obtem-se a solu¸cao analitica para a escolha otima de um dos parametros relacionados com o polo de Kautz, sendo os resultados validos para modelos Wiener/Volterra de qualquer ordem. Apresentam-se ainda resultados de simulacoes que ilustram a metodologia apresentada, bem como a modelagem de um sistema de levitacao magnetica
Abstract: This work investigates the modelling of nonlinear systems using the Wiener/Volterra models with Kautz orthonormal functions. The Volterra models constitute a generalization of the impulse response model to describe nonlinear systems. Such models require a large number of terms for representing the Volterra kernels. However, this complexity can be reduced by using Wiener/Volterra models, in which the kernels are expanded using an orthonormal basis functions. Aspects about selection of the free parameters (poles) characterizing theses functions are discussed, in particular the optimal selection of the complex poles of the Kautz functions. This problem is solved by minimizing the upper bound of the error arising from the truncated approximation of Volterra kernels using Kautz functions. An analytical solution for the optimal choice of one of the parameters related to the Kautz pole is thus obtained, with the results valid for any-order Wiener/Volterra models. Simulations that illustrate the methodology described above are presented. Also, the modelling of a magnetic levitation system is discussed.
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Scussel, Oscar. "Identificação não-paramétrica de sistemas mecânicos usando filtros de Kautz." Universidade Estadual do Oeste do Parana, 2013. http://tede.unioeste.br:8080/tede/handle/tede/1066.
Full textImpulse Response Functions (IRFs) are important in many engineering applications, mainly in structural dynamics and modal analysis involving experimental modal tests. These IRFs can be identified through several methods. Among these, the classical covariance method is one of the most used and it is based on the sum of convolution from the correlation functions between input and output signals known. However, this method is limited because it employs a large number of samples and has drawbacks related to over parametrization. In this sense, this work presentes and review the covariance method expanded in the ortonormal basis Kautz functions, because this alternative way allows to avoid these drawbacks. In order to ilustrate the procedure an algorithm with multiple objective functions to obtain the optimal poles of the Kautz filter is shown. The results are provided through three degree-of-freedom mechanical system simulated and experimental data in a beam to show the advantages, drawbacks, simplicity and efficiency of the proposed approach.
As funções de resposta ao impulso (IRFs) exercem papel de destaque na identificação de sistemas reais quando têm-se o conhecimento dos dados de entrada/saída do sistema. Essas IRFs são relevantes em muitas aplicações de Engenharia, especialmente em análise modal experimental de estruturas. Dentre os métodos para obtenção dessas IRFs, destaca-se o clássico método das covariâncias baseado na soma de convolução das funções de correlação entre os sinais de entrada e saída conhecidos. No entanto, esse método é limitado quando são coletadas muitas amostras e possui algumas desvantagens como efeitos de sobreparametrização. Neste sentido, este trabalho apresenta e revisa o método das covariâncias expandido na base ortonormal de Kautz para aplicações em identificação de sistemas mecânicos, pois essa forma alternativa permite evitar esses efeitos de sobreparametrização. Para obter os pólos ótimos dos filtros de Kautz, emprega-se um algoritmo multi-objetivo. Os resultados são verificados através de um sistema mecânico com três graus de liberdade e em dados experimentais a partir de uma viga na condição livre-livre no qual verificam-se as vantagens, desvantagens, simplicidade e eficiência do método proposto.
Kautz, Stephanie [Verfasser]. "Einfluss der Vernetzung und von Füllstoffen auf diffusionsbestimmte Alterungsprozesse / Stephanie Kautz." Hannover : Gottfried Wilhelm Leibniz Universität Hannover, 2019. http://d-nb.info/1189311550/34.
Full textKautz, Oliver [Verfasser]. "Model Analyses Based on Semantic Differencing and Automatic Model Repair / Oliver Kautz." Düren : Shaker, 2021. http://d-nb.info/1233548298/34.
Full textJin, Mushi. "Topological design and implementation of optical packet switching networks using Kautz graph." Thesis, University of Ottawa (Canada), 2002. http://hdl.handle.net/10393/6224.
Full textKautz, Burkard [Verfasser]. "Fluorescence-based systems for detection of abiotic stresses on horticultural crops / Burkard Kautz." Bonn : Universitäts- und Landesbibliothek Bonn, 2016. http://d-nb.info/1107541867/34.
Full textKautz, Tobias [Verfasser], and Marcus [Akademischer Betreuer] Altfeld. "Charakterisierung von leberresidenten CD49a+ Natürlichen Killerzellen in der humanen Leber / Tobias Kautz ; Betreuer: Marcus Altfeld." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2020. http://d-nb.info/1218236787/34.
Full textBraga, Márcio Feliciano 1983. "Modelos de Volterra = identificação não paramétrica e robusta utilizando funções ortonormais de Kautz e generalizadas." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259982.
Full textDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
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Resumo: Enfoca-se a modelagem de sistemas não-lineares usando modelos de Volterra com bases de funções ortonormais (Orthonormal Basis Functions - OBF) distintas para cada direção do kernel. Os modelos de Volterra constituem uma classe de modelos polinomiais não-recursivos, modelos sem realimentação da saída. Tais modelos são parametrizados por funções multidimensionais, chamadas kernels de Volterra, e representam uma generalização do bem conhecido modelo de resposta ao impulso (FIR) para a descrição de sistemas não-lineares. Como os modelos de Volterra não possuem realimentação do sinal de saída, um número elevado de parâmetros é necessário para representar os kernels de Volterra, especialmente quando o comportamento não-linear do sistema depende fortemente do sinal de saída. No entanto, é possível contornar esta desvantagem por descrever cada kernel por meio de uma expansão em bases de funções ortonormais (OBF). Resultando num modelo que, em geral, possui um número menor de termos para representar o sistema. O modelo resultante, conhecido como modelo OBF-Volterra, pode ser truncado em um número menor de termos se as funções da base forem projetadas adequadamente. O problema reside na questão de como selecionar os polos livres que completamente parametrizam estas funções de forma a reduzir o número de termos a serem utilizados em cada base. Uma abordagem já utilizada envolve a otimização numérica das bases de funções ortonormais usadas para a aproximação de sistemas dinâmicos. Esta estratégia é baseada no cálculo de expressões analíticas para os gradientes da saída dos filtros ortonormais com relação aos polos da base. Estes gradientes fornecem direções de busca exatas para otimizar uma dada base ortonormal. As direções de busca, por sua vez, podem ser usadas como parte de um procedimento de otimização para obter o mínimo de uma função de custo que leva em consideração o erro de estimação da saída do sistema. Esta abordagem considerou apenas os modelos lineares e não-lineares cujas direções dos kernels foram todas parametrizadas por um mesmo conjunto de polos. Neste trabalho, estes resultados foram estendidos de forma a permitir o uso de uma base independente para cada direção dos kernels. Isto permite reduzir ainda mais o erro de truncamento quando as dinâmicas dominantes do kernel ao longo das múltiplas direções são diferentes entre si. As expressões dos gradientes relativas à base de Kautz e à base GOBF são obtidas recursivamente o que permite uma redução no tempo de processamento. Esta metodologia utiliza somente dados de entrada-saída medidos do sistema a ser modelado, isto é, não exige nenhuma informação prévia sobre os kernels de Volterra. Exemplos de simulação ilustram a aplicação dessas abordagens para a modelagem de sistemas não-lineares. Por último, apresentam-se resultados referentes à identificação robusta de modelos não-lineares sob a hipótese de erro desconhecido mas limitado, cujo objetivo é definir os limites superior e inferior dos parâmetros de modelos (intervalos de pertinência paramétrica). É analisado o caso em que se tem informação somente sobre a incerteza na saída do sistema, fornecendo-se o cálculo dos limitantes das incertezas para modelos OBF-Volterra. Estuda-se também os processos que possuem incerteza estruturada, i.e., os parâmetros do modelo, ou os kernels de Volterra, são definidos por meio de intervalos de pertinência e a ordem do modelo é conhecida. Apresenta-se uma solução exata para este problema, eliminando restrições impostas por metodologias anteriores
Abstract: It focuses in the modeling of nonlinear systems using Volterra models with distinct orthonormal basis functions (OBF) to each kernel direction. The Volterra models are a class of nonrecursive polynomial models, models without output feedback. Such models are parameterized by multidimensional functions, called Volterra kernels, they represent a generalization of the well-known impulse response model and are used to describe nonlinear systems. As the Volterra models do not have output feedback, it is required a large number of parameters to represent the Volterra kernels, especially when the nonlinear behavior strongly depends of the output signal. However, such drawback can be overwhelmed by describing each kernel by un expansion in orthonormal basis functions (OBF). Resulting in a model that, in general, requires fewer parameters to represent the system. The resulting model, so-called OBF-Volterra, can be truncated into fewer terms if the basis functions are properly designed. The underlying problem is how to select de free-design poles that fully parameterize these functions in order to reduce the number of terms to be used in each bases. An approach, already used, involves the numeric optimization of orthonormal bases of function used for approximation of dynamic systems. This strategy is based on the computation of analytical expressions for the gradient of the orthonormal filters output with respect to the basis poles. Such gradient provides exact search directions for optimizing the poles of a given orthonormal basis. The search direction can, in turn, be used as part of an optimization procedure to locate the minimum of a cost-function that takes into consideration the estimation error of the system output. Although, that approach took in count only the linear models and nonlinear models which kernels directions were parameterized by a single set of poles. In this work, these results are extended in such a way to allows a use of an independent basis to each kernel direction. It can reduce even more the truncation error when dominant dynamics of the kernel are different along its directions. The gradient expressions to Kautz and GOBF bases are obtained in a recursive way which allows reducing the time processing. This methodology relies solely on input-output data measured from the system to be modeled, i.e., no previous information about the Volterra kernels is required. Simulation examples illustrate the application of this approach to the modeling of nonlinear systems. At last, it is presented some results about robust identification of nonlinear models under the hypothesis of unknown but bounded error, whose aim is to define the upper and lower bounds of the model parameters (parameter uncertainty interval). It is analyzed the case where the information available is about the uncertainty in the system output signal, providing the calculation for the uncertainty intervals to OBF-Volterra models. The process having structured uncertainty, i.e., the models parameters, or the Volterra kernels, are defined by intervals and the model order is known, is also studied. An exact solution to this problem is developed, eliminating restrictions imposed by previous approach
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Daniel, Frédéric. "Sur les communications globales dans les réseaux à topologie de de Bruijn et de Kautz." Toulouse 3, 1996. http://www.theses.fr/1996TOU30248.
Full textBooks on the topic "Kautz"
Öfele, Martin. General August Valentin Kautz: Erinnerungen an den Bürgerkrieg. Wyk auf Föhr, Germany: Verlag für Amerikanistik, 1997.
Find full textKautz, Gerhard. Volhynia to Canada: The Friedrich Kautz family history. Greely, Ont: G.W. Kautz, 2010.
Find full textAugust Valentine Kautz, USA: Biography of a Civil War general. Jefferson, N.C: McFarland & Co., Publishers, 2008.
Find full textVittoria, Coen, Liebmann Lisa, and Trento (Italy). Galleria civica di arte contemporanea., eds. Alex Katz. Torino: Hopefulmonster, 1999.
Find full textBook chapters on the topic "Kautz"
Harbane, Rabah. "Fault-tolerant Kautz networks." In DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 201–10. Providence, Rhode Island: American Mathematical Society, 1995. http://dx.doi.org/10.1090/dimacs/021/14.
Full textden Brinker, Albertus C., and Harm J. W. Belt. "Using Kautz Models in Model Reduction." In Signal Analysis and Prediction, 185–96. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8_13.
Full textSalinger, Petr, and Pavel Tvrdík. "All-to-All Scatter in Kautz networks." In Euro-Par’98 Parallel Processing, 1057–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0057967.
Full textDu, Ding-Zhu, Feng Cao, and D. Frank Hsu. "De Bruijn Digraphs, Kautz Digraphs, and Their Generalizations." In Combinatorial Network Theory, 65–105. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2491-2_3.
Full textSchmölders, Ralf. "„Im Schatten der Schlote“ — Notizen über Heinrich Kautz." In Jugend 1900–1970, 45–55. Wiesbaden: VS Verlag für Sozialwissenschaften, 1991. http://dx.doi.org/10.1007/978-3-322-95945-4_4.
Full textLi, Dongsheng, Xicheng Lu, and Jinshu Su. "Graph-Theoretic Analysis of Kautz Topology and DHT Schemes." In Lecture Notes in Computer Science, 308–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30141-7_45.
Full textGrigorious, Cyriac, Thomas Kalinowski, and Sudeep Stephen. "On the Power Domination Number of de Bruijn and Kautz Digraphs." In Lecture Notes in Computer Science, 264–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78825-8_22.
Full textBernabei, F., V. De Simone, L. Gratta, and M. Listanti. "Shuffle vs. Kautz/De Bruijn Logical Topologies for Multihop Networks: a Throughput Comparison." In Broadband Communications, 271–82. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-0-387-34987-9_23.
Full textKikuchi, Yosuke, and Yukio Shibata. "On the Domination Numbers of Generalized de Bruijn Digraphs and Generalized Kautz Digraphs." In Lecture Notes in Computer Science, 400–408. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44679-6_45.
Full textChan, Swee Hong, Henk D. L. Hollmann, and Dmitrii V. Pasechnik. "Critical groups of generalized de Bruijn and Kautz graphs and circulant matrices over finite fields." In The Seventh European Conference on Combinatorics, Graph Theory and Applications, 97–104. Pisa: Scuola Normale Superiore, 2013. http://dx.doi.org/10.1007/978-88-7642-475-5_16.
Full textConference papers on the topic "Kautz"
Taheri, Ehsan. "Kautz-based adaptive control." In 2008 3rd International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2008. http://dx.doi.org/10.1109/isccsp.2008.4537299.
Full textYu, Jiguo, Jingjing Song, Wenjun Liu, Li Zhao, and Baoxiang Cao. "KZCAN: A Kautz Based Content-Addressable Network." In Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007). IEEE, 2007. http://dx.doi.org/10.1109/snpd.2007.426.
Full textden Brinker, A. C., and B. E. Sarroukh. "Optimal parameters in Laguerre and Kautz series." In 1999 European Control Conference (ECC). IEEE, 1999. http://dx.doi.org/10.23919/ecc.1999.7100079.
Full textMingzhu Xu, Yiping Jiang, Jinzhi Liu, and Shenshan Li. "Main steam temperature's adaptive control with Kautz model." In 2010 8th World Congress on Intelligent Control and Automation (WCICA 2010). IEEE, 2010. http://dx.doi.org/10.1109/wcica.2010.5555038.
Full textMingzhu Xu, Shenshan Li, and Zhiping Xu. "Predictive functional control on Kautz with genetic optimization." In 2008 7th World Congress on Intelligent Control and Automation. IEEE, 2008. http://dx.doi.org/10.1109/wcica.2008.4594550.
Full textSabbaghi-Nadooshan, Reza, and Hamid Sarbazi-Azad. "The Kautz mesh: A new topology for SoCs." In 2008 International SoC Design Conference (ISOCC). IEEE, 2008. http://dx.doi.org/10.1109/socdc.2008.4815632.
Full textGiglmayr, Josef. "Kautz topologies for all-optical self-routing networks." In Photonics East '95, edited by Vincent W. S. Chan, Robert A. Cryan, and John M. Senior. SPIE, 1995. http://dx.doi.org/10.1117/12.227828.
Full textIsaksson, M., and D. Ronnow. "A Kautz-Volterra Behavioral Model for RF Power Amplifiers." In 2006 IEEE MTT-S International Microwave Symposium Digest. IEEE, 2006. http://dx.doi.org/10.1109/mwsym.2006.249598.
Full textXu Mingzhu, Jiang Yiping, Pan Cunzhi, and Wang Zhanzhong. "An incremental predictive functional control based on Kautz model." In 2009 International Conference on Sustainable Power Generation and Supply. SUPERGEN 2009. IEEE, 2009. http://dx.doi.org/10.1109/supergen.2009.5347923.
Full textHuang, Feng, and Jingbo Dai. "Fast data dissemination in Kautz-based modular datacenter network." In 2012 International Conference on Systems and Informatics (ICSAI). IEEE, 2012. http://dx.doi.org/10.1109/icsai.2012.6223348.
Full textReports on the topic "Kautz"
Kindle, Nicole. The Many Wives of General August V. Kautz: Colonization in the Pacific Northwest, 1853-1895. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7231.
Full textWashburn, Alan. Katz Distributions, with Applications to Minefield Clearance. Fort Belvoir, VA: Defense Technical Information Center, March 1996. http://dx.doi.org/10.21236/ada307317.
Full textBrooks, G. R., and J. A. Pilon. Ground penetrating radar survey of the Katz slide, southwestern British Columbia. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1995. http://dx.doi.org/10.4095/202755.
Full textNeumark, David, and William Wascher. Employment Effects of Minimum and Subminimum Wages: Reply to Card, Katz and Krueger. Cambridge, MA: National Bureau of Economic Research, December 1993. http://dx.doi.org/10.3386/w4570.
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