Littérature scientifique sur le sujet « Echo-optimization »
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Articles de revues sur le sujet "Echo-optimization"
Zhang, Q. « Echo optimization of cardiac resynchronization therapy ». International Journal of Cardiology 125 (février 2008) : S15. http://dx.doi.org/10.1016/s0167-5273(08)70158-x.
Texte intégralJohnson, G., et E. X. Wu. « Sensitivity Optimization of Echo Times and Data Sampling Times for Spin-Echo Spectroscopy ». Journal of Magnetic Resonance, Series B 105, no 3 (novembre 1994) : 238–41. http://dx.doi.org/10.1006/jmrb.1994.1130.
Texte intégralR., Vincy, et J. William. « Optimization of Acoustic Echo and Noise Reduction in Non Stationary Environment ». International Journal of Advance Research and Innovation 3, no 1 (2015) : 104–8. http://dx.doi.org/10.51976/ijari.311519.
Texte intégralThiede, Luca Anthony, et Ulrich Parlitz. « Gradient based hyperparameter optimization in Echo State Networks ». Neural Networks 115 (juillet 2019) : 23–29. http://dx.doi.org/10.1016/j.neunet.2019.02.001.
Texte intégralGowland, P. A., et R. Bowtell. « Theoretical optimization of multi-echo fMRI data acquisition ». Physics in Medicine and Biology 52, no 7 (2 mars 2007) : 1801–13. http://dx.doi.org/10.1088/0031-9155/52/7/003.
Texte intégralVidarsson, Logi, Steven M. Conolly, Kelvin O. Lim, Garry E. Gold et John M. Pauly. « Echo time optimization for linear combination myelin imaging ». Magnetic Resonance in Medicine 53, no 2 (2005) : 398–407. http://dx.doi.org/10.1002/mrm.20360.
Texte intégralXue, Yu, Qi Zhang et Adam Slowik. « Automatic topology optimization of echo state network based on particle swarm optimization ». Engineering Applications of Artificial Intelligence 117 (janvier 2023) : 105574. http://dx.doi.org/10.1016/j.engappai.2022.105574.
Texte intégralHu, Haofei, Chongyi Fan et Xiaotao Huang. « Radar Target Detection Based on Waveform Design under Atmospheric Attenuation ». Journal of Physics : Conference Series 2290, no 1 (1 juin 2022) : 012071. http://dx.doi.org/10.1088/1742-6596/2290/1/012071.
Texte intégralArmenio, Luca Bugliari, Lorenzo Fagiano, Enrico Terzi, Marcello Farina et Riccardo Scattolini. « Optimal Training of Echo State Networks via Scenario Optimization ». IFAC-PapersOnLine 53, no 2 (2020) : 5183–88. http://dx.doi.org/10.1016/j.ifacol.2020.12.1187.
Texte intégralLiu, Junxiu, Tiening Sun, Yuling Luo, Su Yang, Yi Cao et Jia Zhai. « Echo state network optimization using binary grey wolf algorithm ». Neurocomputing 385 (avril 2020) : 310–18. http://dx.doi.org/10.1016/j.neucom.2019.12.069.
Texte intégralThèses sur le sujet "Echo-optimization"
RUEDA, CAMILO VELASCO. « ESNPREDICTOR : TIME SERIES FORECASTING APPLICATION BASED ON ECHO STATE NETWORKS OPTIMIZED BY GENETICS ALGORITHMS AND PARTICLE SWARM OPTIMIZATION ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=24785@1.
Texte intégralCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
PROGRAMA DE EXCELENCIA ACADEMICA
A previsão de séries temporais é fundamental na tomada de decisões de curto, médio e longo prazo, em diversas áreas como o setor elétrico, a bolsa de valores, a meteorologia, entre outros. Tem-se na atualidade uma diversidade de técnicas e modelos para realizar essas previsões, mas as ferramentas estatísticas são as mais utilizadas principalmente por apresentarem um maior grau de interpretabilidade. No entanto, as técnicas de inteligência computacional têm sido cada vez mais aplicadas em previsão de séries temporais, destacando-se as Redes Neurais Artificiais (RNA) e os Sistemas de Inferência Fuzzy (SIF). Recentemente foi criado um novo tipo de RNA, denominada Echo State Networks (ESN), as quais diferem das RNA clássicas por apresentarem uma camada escondida com conexões aleatórias, denominada de Reservoir (Reservatório). Este Reservoir é ativado pelas entradas da rede e pelos seus estados anteriores, gerando o efeito de Echo State (Eco), fornecendo assim um dinamismo e um desempenho melhor para tarefas de natureza temporal. Uma dificuldade dessas redes ESN é a presença de diversos parâmetros, tais como Raio Espectral, Tamanho do Reservoir e a Percentual de Conexão, que precisam ser calibrados para que a ESN forneça bons resultados. Portanto, este trabalho tem como principal objetivo o desenvolvimento de uma ferramenta computacional capaz de realizar previsões de séries temporais, baseada nas ESN, com ajuste automático de seus parâmetros por Particle Swarm Optimization (PSO) e Algoritmos Genéticos (GA), facilitando a sua utilização pelo usuário. A ferramenta computacional desenvolvida oferece uma interface gráfica intuitiva e amigável, tanto em termos da modelagem da ESN, quanto em termos de realização de eventuais pré-processamentos na série a ser prevista.
The time series forecasting is critical to decision making in the short, medium and long term in several areas such as electrical, stock market, weather and industry. Today exist different techniques to model this forecast, but statistics are more used, because they have a bigger interpretability, due by the mathematic models created. However, intelligent techniques are being more applied in time series forecasting, where the principal models are the Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS). A new type of ANN called Echo State Networks (ESN) was created recently, which differs from the classic ANN in a randomly connected hidden layer called Reservoir. This Reservoir is activated by the network inputs, and the historic of the reservoir activations generating so, the Echo State and giving to the network more dynamism and a better performance in temporal nature tasks. One problem with these networks is the presence of some parameters as, Spectral Radius, Reservoir Size and Connection Percent, which require calibration to make the network provide positive results. Therefore the aim of this work is to develop a computational application capable to do time series forecasting, based on ESN, with automatic parameters adjustment by Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), facilitating its use by the user. The developed computational tool offers an intuitive and friendly interface, both in terms of modeling the ESN, and in terms of achievement of possible pre-process on the series to be forecasted.
Prokudaylo, Sergey. « Calculations for neutron spin echo optimization of the magnetic field geometries and preparations and analysis of experiments on crystal lattice dynamics / ». [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=97258840X.
Texte intégralAilleres, Norbert. « Etude comparative des séquences d'échos de spins, d'échos stimulés et d'échos de gradients en I. R. M : optimisation du contraste ». Toulouse 3, 1992. http://www.theses.fr/1992TOU30254.
Texte intégralCharles, Adam Shabti. « Dynamics and correlations in sparse signal acquisition ». Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53592.
Texte intégralLaule, Cornelia. « Optimization of a 48 echo magnetic resonance imaging sequence using variable TR data acquisition ». Thesis, 2000. http://hdl.handle.net/2429/11310.
Texte intégralDanagoulian, Giovanna Selvaggi. « Quantum mechanical analysis and numerical optimization of a "zero-field" spin echo small angle neutron scattering instrument / ». 2007. 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:3301123.
Texte intégralSource: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1301. Adviser: Brent J. Heuser. Includes bibliographical references (leaves 105-106) Available on microfilm from Pro Quest Information and Learning.
Prokudaylo, Sergey [Verfasser]. « Calculations for neutron spin echo : optimization of the magnetic field geometries and preparations and analysis of experiments on crystal lattice dynamics / Sergey Prokudaylo ». 2004. http://d-nb.info/97258840X/34.
Texte intégralSantanni, Fabio. « Molecular approaches for the optimization of electron spin-based quantum bits and quantum logic gates ». Doctoral thesis, 2022. http://hdl.handle.net/2158/1262928.
Texte intégralChapitres de livres sur le sujet "Echo-optimization"
Zhang, Jinwei, Hang Zhang, Chao Li, Pascal Spincemaille, Mert Sabuncu, Thanh D. Nguyen et Yi Wang. « Temporal Feature Fusion with Sampling Pattern Optimization for Multi-echo Gradient Echo Acquisition and Image Reconstruction ». Dans Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 232–42. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87231-1_23.
Texte intégralWang, Yan, Chao Wang et Jie Li. « The Optimization of Radar Echo Pulse Compression Algorithm Based on DSP ». Dans Lecture Notes in Electrical Engineering, 247–55. Singapore : Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3229-5_27.
Texte intégralPedersen, Peder C., et Li Wan. « Optimization of Pulse-Echo Array Transducer System for Identification of Specified Targets ». Dans Acoustical Imaging, 373–80. Boston, MA : Springer US, 2002. http://dx.doi.org/10.1007/978-1-4419-8606-1_47.
Texte intégralSong, Ke. « Optimization of Echo Parameter in Intelligent Instrument Under the Condition of Numerical Stability ». Dans Advances in Intelligent Systems and Computing, 1234–38. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15235-2_165.
Texte intégralAbadlia, I., L. Hassaine, F. Abdoune et A. Beddar. « Energy Management Strategy and Optimization of a MicroGrid System Based on Echo State Networks ». Dans Artificial Intelligence and Renewables Towards an Energy Transition, 168–77. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63846-7_17.
Texte intégralSong, Qingsong, Zuren Feng et Yonggang Wang. « Stable Training Method for Echo State Networks Running in Closed-Loop Based on Particle Swarm Optimization Algorithm ». Dans Neural Information Processing, 253–62. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10684-2_28.
Texte intégralStavroulakis, Georgios E. « Global optimization for crack identification : impact-echo experiments ». Dans Combinatorial and Global Optimization, 317–31. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812778215_0021.
Texte intégral« Joint Optimization of Acoustic Echo Cancellation and Adaptive Beamforming ». Dans Topics in Acoustic Echo and Noise Control, 19–50. Berlin, Heidelberg : Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-33213-8_2.
Texte intégralVieillard-Baron, Antoine. « Haemodynamic assessments in mechanically ventilated patients ». Dans Oxford Textbook of Advanced Critical Care Echocardiography, sous la direction de Anthony McLean, Stephen Huang et Andrew Hilton, 321–26. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198749288.003.0025.
Texte intégralBalik, Martin. « Acute respiratory failure ». Dans Oxford Textbook of Advanced Critical Care Echocardiography, sous la direction de Anthony McLean, Stephen Huang et Andrew Hilton, 287–94. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198749288.003.0021.
Texte intégralActes de conférences sur le sujet "Echo-optimization"
Lin, Zhenliang, Qikang Li, Rui Wang, Guobin Li et Jie Luo. « Optimization of Acoustic Noise for Single-Shot Echo-Planar Imaging by Varying Echo Spacing ». Dans ICBBE '20 : 2020 7th International Conference on Biomedical and Bioinformatics Engineering. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3444884.3444895.
Texte intégralCai, Mao, Xingming Fan, Chao Wang, Linlin Gao et Xin Zhang. « Research for Parameters Optimization of Echo State Network ». Dans 2018 11th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2018. http://dx.doi.org/10.1109/iscid.2018.10142.
Texte intégralFang, Wenxing, et Yiping P. Du. « Optimization of 3D time-of-flight MR angiography with shortened echo time and dual-echo acquisition ». Dans 2013 6th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2013. http://dx.doi.org/10.1109/bmei.2013.6746906.
Texte intégralZhang, Zhaozhao, Xiaohui Wang et Yingqin Zhu. « Echo State Network Optimization Based on Improved Fireworks Algorithm ». Dans 2022 7th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2022. http://dx.doi.org/10.1109/icivc55077.2022.9886609.
Texte intégralWenting Deng, S. Reeves et D. B. Twieg. « Fast magnetic resonance spectroscopic imaging using echo-time optimization ». Dans 2010 IEEE Nuclear Science Symposium and Medical Imaging Conference (2010 NSS/MIC). IEEE, 2010. http://dx.doi.org/10.1109/nssmic.2010.5874152.
Texte intégral« Evolutionary Optimization of Echo State Networks : Multiple Motor Pattern Learning ». Dans 6th International Workshop on Artificial Neural Networks and Intelligent Information Processing. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003027500630071.
Texte intégralMerabti, Hocine, et Daniel Massicotte. « Robust nonlinear acoustic echo cancellation using a metaheuristic optimization approach ». Dans 2015 IEEE International Conference on Digital Signal Processing (DSP). IEEE, 2015. http://dx.doi.org/10.1109/icdsp.2015.7251879.
Texte intégralRomoli, Laura, Stefania Cecchi, Francesco Piazza, Danilo Comminiello, Michele Scarpiniti et Aurelio Uncini. « An interactive optimization procedure for stereophonic acoustic echo cancellation systems ». Dans 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280444.
Texte intégralFan, Hsiao-Tien, Wei Wang et Zhanpeng Jin. « Performance optimization of echo state networks through principal neuron reinforcement ». Dans 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966058.
Texte intégralMaat, Jacob Reinier, Nikos Gianniotis et Pavlos Protopapas. « Efficient Optimization of Echo State Networks for Time Series Datasets ». Dans 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489094.
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