Academic literature on the topic 'Automatic networks'
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Journal articles on the topic "Automatic networks"
Dao-Thi, Thu-Ha, and Jean Mairesse. "Zero-Automatic Networks." Discrete Event Dynamic Systems 18, no. 4 (September 16, 2008): 499–536. http://dx.doi.org/10.1007/s10626-008-0048-1.
Full textDroste, Peter, Wolfgang Wiechert, and Katharina Nöh. "Semi-automatic drawing of metabolic networks." Information Visualization 11, no. 3 (August 22, 2011): 171–87. http://dx.doi.org/10.1177/1473871611413565.
Full textHou, Tingting, and Zamira Madina. "Automatic Classification of Basic Nursing Teaching Resources Based on the Fusion of Multiple Neural Networks." Mathematical Problems in Engineering 2022 (February 21, 2022): 1–7. http://dx.doi.org/10.1155/2022/7176111.
Full textTriantafyllis, N., E. Sokos, and A. Ilias. "Automatic moment tensor determination for the Hellenic Unified Seismic Network." Bulletin of the Geological Society of Greece 47, no. 3 (December 21, 2016): 1308. http://dx.doi.org/10.12681/bgsg.10912.
Full textSharygin, Mikhail, Vladimir Vukolov, and Anton Petrov. "Adaptive multivariable relay protection of reconfigurable distribution networks." E3S Web of Conferences 139 (2019): 01048. http://dx.doi.org/10.1051/e3sconf/201913901048.
Full textBarinov, Roman, Vasiliy Gai, George Kuznetsov, and Vladimir Golubenko. "Automatic Evaluation of Neural Network Training Results." Computers 12, no. 2 (January 20, 2023): 26. http://dx.doi.org/10.3390/computers12020026.
Full textWang, Zibo, Yaofang Zhang, Zhiyao Liu, Xiaojie Wei, Yilu Chen, and Bailing Wang. "An Automatic Planning-Based Attack Path Discovery Approach from IT to OT Networks." Security and Communication Networks 2021 (October 31, 2021): 1–18. http://dx.doi.org/10.1155/2021/1444182.
Full textMossin, Eduardo André, Dennis Brandão, Guilherme Serpa Sestito, and Renato Veiga Torres. "Automatic Diagnosis for Profibus Networks." Journal of Control, Automation and Electrical Systems 27, no. 6 (July 25, 2016): 658–69. http://dx.doi.org/10.1007/s40313-016-0261-3.
Full textGurskiy, A. A., A. E. Goncharenko, and S. M. Dubna. "AUTOMATIC SYNTHESIS OF PETRI NETS AT TUNING UP OF THE COORDINATING AUTOMATIC CONTROL SYSTEMS." ELECTRICAL AND COMPUTER SYSTEMS 33, no. 108 (November 30, 2020): 34–44. http://dx.doi.org/10.15276/eltecs.32.108.2020.4.
Full textWang, Shuai, and Chunwu Liu. "Automatic Modulation Classification with Neural Networks via Knowledge Distillation." Electronics 11, no. 19 (September 22, 2022): 3018. http://dx.doi.org/10.3390/electronics11193018.
Full textDissertations / Theses on the topic "Automatic networks"
Grau, Leguia Marc. "Automatic reconstruction of complex dynamical networks." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/666631.
Full textUn problema principal de la ciencia de redes es cómo reconstruir (inferir) la topología de una red real usando la señales medidas de sus unidades internas. Entender la arquitectura de redes complejas es clave, no solo para entender su funcionamiento pero también para predecir y controlar su comportamiento. Los métodos existentes se focalizan en la detección de redes no direccionales y normalmente requieren fuertes suposicio- nes sobre el sistema. Sin embargo, muchos de estos métodos no pueden ser aplicados en redes con conexiones direccionales. Para abordar este problema, en esta tesis estudiamos la reconstrucción de redes direccio- nales. En concreto, desarrollamos un método de reconstrucción basado en modelos que combina estadísticas de correlaciones de derivadas con recocido simulado. Además, desarrollamos un método basado en datos cimentado en una medida d’interdependencia no lineal. Este método permite inferir la topología de redes direccionales de osciladores caóticos de Lorenz para un subrango de la fuerza de acoplamiento y densidad de la red. Finalmente, aplicamos el método basado en datos a grabaciones electroencefalográficas de un paciente con epilepsia. Las redes cerebra- les funcionales obtenidas usando este método son consistentes con la información médica disponible.
A foremost problem in network science is how to reconstruct (infer) the topology of a real network from signals measured from its internal units. Grasping the architecture of complex networks is key, not only to understand their functioning, but also to predict and control their behaviour. Currently available methods largely focus on the detection of links of undirected networks and often require strong assumptions about the system. However, many of these methods cannot be applied to networks with directional connections. To address this problem, in this doctoral work we focus at the inference of directed networks. Specifically, we develop a model-based network reconstruction method that combines statistics of derivative-variable correlations with simulated annealing. We furthermore develop a data-driven reconstruction method based on a nonlinear interdependence measure. This method allows one to infer the topology of directed networks of chaotic Lorenz oscillators for a subrange of the coupling strength and link density. Finally, we apply the data-driven method to multichannel electroencephalographic recordings from an epilepsy patient. The functional brain networks obtained from this approach are consistent with the available medical information.
Glöckner, Reinhard Jörg. "Power transfer optimised automatic matching networks." Thesis, Northumbria University, 2009. http://nrl.northumbria.ac.uk/1622/.
Full textLi, Ping. "Neural networks for automatic arc welding." Thesis, University of Liverpool, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284264.
Full textRivera, Corban G. "Automatic Reconstruction of the Building Blocks of Molecular Interaction Networks." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/28752.
Full text- cellular wiring diagrams can be decomposed into overlapping modules, where each module is a set of coherently-interacting molecules and
- a cell responds to a stress or a stimulus by appropriately modulating the activities of a subset of these modules.
- Given a wiring diagram and genome-wide gene expression data measured after the application of a stress or in a disease state, compute the active network of molecular interactions perturbed by the stress or the disease.
- Given the active networks for multiple stresses, stimuli, or diseases, compute a set of network legos, which are molecular modules with the property that each active network can be expressed as an appropriate combination of a subset of modules.
Ph. D.
Prager, Richard William. "Parallel processing networks for automatic speech recognition." Thesis, University of Cambridge, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238443.
Full textLee, George J. (George Janbing) 1979. "Automatic service selection in dynamic wireless networks." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87434.
Full textCrestel, Léopold. "Neural networks for automatic musical projective orchestration." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS625.
Full textOrchestration is the art of composing a musical discourse over a combinatorial set of instrumental possibilities. For centuries, musical orchestration has only been addressed in an empirical way, as a scientific theory of orchestration appears elusive. In this work, we attempt to build the first system for automatic projective orchestration, and to rely on machine learning. Hence, we start by formalizing this novel task. We focus our effort on projecting a piano piece onto a full symphonic orchestra, in the style of notable classic composers such as Mozart or Beethoven. Hence, the first objective is to design a system of live orchestration, which takes as input the sequence of chords played by a pianist and generate in real-time its orchestration. Afterwards, we relax the real-time constraints in order to use slower but more powerful models and to generate scores in a non-causal way, which is closer to the writing process of a human composer. By observing a large dataset of orchestral music written by composers and their reduction for piano, we hope to be able to capture through statistical learning methods the mechanisms involved in the orchestration of a piano piece. Deep neural networks seem to be a promising lead for their ability to model complex behaviour from a large dataset and in an unsupervised way. More specifically, in the challenging context of symbolic music which is characterized by a high-dimensional target space and few examples, we investigate autoregressive models. At the price of a slower generation process, auto-regressive models allow to account for more complex dependencies between the different elements of the score, which we believe to be of the foremost importance in the case of orchestration
Newman, Rhys A. "Automatic learning in computer vision." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390526.
Full textWinsnes, Casper. "Automatic Subcellular Protein Localization Using Deep Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189991.
Full textHowell, Andrew Jonathan. "Automatic face recognition using radial basis function networks." Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.241635.
Full textBooks on the topic "Automatic networks"
Bowen, Jacqueline D. Automatic fingerprint pattern classification using neutral networks. London: Home Office, Science and Technology Group, 1992.
Find full textRost, Burkhard. Automatic sensor networks of verifying disarmament of aircraft. Bochum: UVB Universitätsverlag Dr. N. Brockmeyer, 1991.
Find full textCan, Vuran Mehmet, ed. Wireless sensor networks. Chichester, West Sussex, U.K: Wiley, 2010.
Find full textAzzouz, Elsayed Elsayed. Automatic modulation recognition of communication signals. Boston: Kluwer Academic Publishers, 1996.
Find full textDib, Mohammed. Automatic Speech Recognition of Arabic Phonemes with Neural Networks. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-97710-2.
Full textAzzouz, Elsayed Elsayed. Automatic Modulation Recognition of Communication Signals. Boston, MA: Springer US, 1996.
Find full textKocheisen, Michael. Neutral networks in photofinishing. Konstanz: Hartung-Gorre, 1997.
Find full textZheng-Hua, Tan, and Lindberg Børge, eds. Automatic speech recognition on mobile devices and over communication networks. London: Springer, 2008.
Find full textTan, Zheng-Hua, and Børge Lindberg. Automatic Speech Recognition on Mobile Devices and over Communication Networks. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-143-5.
Full textSophie, Tarbouriech, Abdallah C. T, and Chiasson John Nelson, eds. Advances in communication control networks. Berlin: Springer, 2005.
Find full textBook chapters on the topic "Automatic networks"
Kłosowski, Piotr, and Adam Dustor. "Automatic Speech Segmentation for Automatic Speech Translation." In Computer Networks, 466–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38865-1_47.
Full textYu, Dong, and Li Deng. "Deep Neural Networks." In Automatic Speech Recognition, 57–77. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5779-3_4.
Full textGotzhein, Reinhard. "Automatic Topology Detection." In Computer Communications and Networks, 79–98. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33319-5_4.
Full textYu, Dong, and Li Deng. "Adaptation of Deep Neural Networks." In Automatic Speech Recognition, 193–215. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5779-3_11.
Full textYu, Dong, and Li Deng. "Recurrent Neural Networks and Related Models." In Automatic Speech Recognition, 237–66. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5779-3_13.
Full textCzyszczoń, Adam, and Aleksander Zgrzywa. "Automatic Customer Segmentation for Social CRM Systems." In Computer Networks, 552–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38865-1_55.
Full textYu, Dong, and Li Deng. "Feature Representation Learning in Deep Neural Networks." In Automatic Speech Recognition, 157–75. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5779-3_9.
Full textCzyszczoń, Adam, and Aleksander Zgrzywa. "Automatic RESTful Web Service Identification and Information Extraction." In Computer Networks, 318–27. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07941-7_32.
Full textWehenkel, Louis A. "Artificial Neural Networks." In Automatic Learning Techniques in Power Systems, 71–98. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5451-6_4.
Full textYu, Dong, and Li Deng. "Representation Sharing and Transfer in Deep Neural Networks." In Automatic Speech Recognition, 219–35. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5779-3_12.
Full textConference papers on the topic "Automatic networks"
Dao-Thi, Thu-Ha, and Jean Mairesse. "Zero-automatic networks." In the 1st international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1190095.1190098.
Full textSingh, Chandan Kumar, Anima Majumder, Swagat Kumar, and Laxmidhar Behera. "Deep Network based Automatic Annotation for Warehouse Automation." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489424.
Full textCornelissen, Willy, and Maurício Loureiro. "Automatic onset detection using convolutional neural networks." In Simpósio Brasileiro de Computação Musical. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbcm.2019.10446.
Full textLiu, Huajie, and Jian (John) Dong. "Artificial Neural Networks for Automatic Dispatching Rule Selection." In ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium collocated with the ASME 1995 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/cie1995-0832.
Full textMatic, Ivana D., Antonio Pedro Oliveira, and Amilcar Cardoso. "Automatic melody generation using Neural Networks and Cellular Automata." In 2012 11th Symposium on Neural Network Applications in Electrical Engineering (NEUREL 2012). IEEE, 2012. http://dx.doi.org/10.1109/neurel.2012.6419972.
Full textRizzi, A., F. M. F. Mascioli, and G. Martinelli. "Automatic training of ANFIS networks." In Proceedings of 8th International Fuzzy Systems Conference. IEEE, 1999. http://dx.doi.org/10.1109/fuzzy.1999.790153.
Full textSharma, S., D. Staessens, D. Colle, M. Pickavet, and P. Demeester. "Automatic bootstrapping of OpenFlow networks." In 2013 IEEE 19th International Workshop on Local & Metropolitan Area Networks (LANMAN). IEEE, 2013. http://dx.doi.org/10.1109/lanman.2013.6528283.
Full textGeyer, Fabien, and Georg Carle. "Towards automatic performance optimization of networks using machine learning." In 2016 17th International Telecommunications Network Strategy and Planning Symposium (Networks). IEEE, 2016. http://dx.doi.org/10.1109/netwks.2016.7751147.
Full textLi, C. James, and T. Y. Huang. "Automatic Structure and Parameter Training Methods for Modeling of Mechanical System by Recurrent Neural Networks." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-0402.
Full textPudovkin, Mikhail I., Ake Steen, N. V. Nikolaev, O. I. Kornilov, Urban Brandstrom, Bjorn Gustavsson, and Peter Rydesater. "Automatic recognition of auroral forms." In Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, edited by Thomas Lindblad, Mary Lou Padgett, and Jason M. Kinser. SPIE, 1999. http://dx.doi.org/10.1117/12.343030.
Full textReports on the topic "Automatic networks"
Hillard, D., M. Ostendorf, A. Stolcke, Y. Liu, and E. Shriberg. Improving Automatic Sentence Boundary Detection with Confusion Networks. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada460954.
Full textLenz, Lutz. Automatic Tuning of Integrated Filters Using Neural Networks. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6488.
Full textShah, S., and M. Yip. Extreme Networks' Ethernet Automatic Protection Switching (EAPS) Version 1. RFC Editor, October 2003. http://dx.doi.org/10.17487/rfc3619.
Full textGreamo, Chris. Automatic Identification & Mitigation of Unauthorized Information Leaking from Tactical Mobile Networks. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada586817.
Full textStevenson, G. Analysis of Pre-Trained Deep Neural Networks for Large-Vocabulary Automatic Speech Recognition. Office of Scientific and Technical Information (OSTI), July 2016. http://dx.doi.org/10.2172/1289367.
Full textCroft, Elizabeth, and Barbara Spencer. Fees and Surcharging in automatic teller machine networks: Non-bank ATM providers versus large banks. Cambridge, MA: National Bureau of Economic Research, August 2003. http://dx.doi.org/10.3386/w9883.
Full textArhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, April 2021. http://dx.doi.org/10.31979/mti.2021.1943.
Full textDannemann Dugick, Fransiska, Sarah Albert, Gil Averbuch, and Stephen Arrowsmith. Utilizing the Dynamic Networks Data Processing and Analysis Experiment (DNE18) to Establish Methodologies for the Comparison of Automatic Infrasonic Signal Detectors. Office of Scientific and Technical Information (OSTI), November 2021. http://dx.doi.org/10.2172/1832306.
Full textYaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko, and Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3683.
Full textBeiker, Sven. Unsettled Issues in Remote Operation for On-road Driving Automation. SAE International, December 2021. http://dx.doi.org/10.4271/epr2021028.
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