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Статті в журналах з теми "Neural forms"
Di Cesare, Giuseppe, Marzio Gerbella, and Giacomo Rizzolatti. "The neural bases of vitality forms." National Science Review 7, no. 1 (January 1, 2020): 202–13. http://dx.doi.org/10.1093/nsr/nwz187.
Повний текст джерелаIbarra, Oscar H., Andrei Păun, Gheorghe Păun, Alfonso Rodríguez-Patón, Petr Sosík, and Sara Woodworth. "Normal forms for spiking neural P systems." Theoretical Computer Science 372, no. 2-3 (March 2007): 196–217. http://dx.doi.org/10.1016/j.tcs.2006.11.025.
Повний текст джерелаGrossberg, Stephen. "Filling-in the forms." Behavioral and Brain Sciences 21, no. 6 (December 1998): 758–59. http://dx.doi.org/10.1017/s0140525x98341758.
Повний текст джерелаMorgan, Peter, Bruce Curry, and Malcolm Beynon. "Comparing neural network approximations for different functional forms." Expert Systems 16, no. 2 (May 1999): 60–71. http://dx.doi.org/10.1111/1468-0394.00096.
Повний текст джерелаLai, Tzu-Hsien, Ekaterina Protsenko, Yu-Chen Cheng, Marco L. Loggia, Gianluca Coppola, and Wei-Ta Chen. "Neural Plasticity in Common Forms of Chronic Headaches." Neural Plasticity 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/205985.
Повний текст джерелаMichaelides, Panayotis G., Angelos T. Vouldis, and Efthymios G. Tsionas. "Globally flexible functional forms: The neural distance function." European Journal of Operational Research 206, no. 2 (October 2010): 456–69. http://dx.doi.org/10.1016/j.ejor.2010.02.013.
Повний текст джерелаSchneidereit, Toni, and Michael Breuß. "Collocation polynomial neural forms and domain fragmentation for solving initial value problems." Neural Computing and Applications 34, no. 9 (December 27, 2021): 7141–56. http://dx.doi.org/10.1007/s00521-021-06860-4.
Повний текст джерелаBuchelnikov, М. А., М. U. Sidorova, О. V. Spirenkova, and М. Е. Nikulina. "Use of artificial neural networks for recognizing cannel forms." Interexpo GEO-Siberia 4 (May 18, 2022): 148–51. http://dx.doi.org/10.33764/2618-981x-2022-4-148-151.
Повний текст джерелаSazonova, N. G., T. A. Makarenko, and A. N. Narkevich. "Predicting various forms of endometriosis using artificial neural networks." Siberian Journal of Clinical and Experimental Medicine 35, no. 4 (December 25, 2020): 143–49. http://dx.doi.org/10.29001/2073-8552-2020-35-4-143-149.
Повний текст джерелаCohen, J. R., and R. A. Poldrack. "The Neural Correlates of Multiple Forms of Self-Control." NeuroImage 47 (July 2009): S178. http://dx.doi.org/10.1016/s1053-8119(09)71941-x.
Повний текст джерелаДисертації з теми "Neural forms"
Bonham, James Robert. "A study of molecular forms of the cholinesterases with particular reference to Hirschsprung's disease and neural tube defects." Thesis, University of Newcastle Upon Tyne, 1986. http://hdl.handle.net/10443/545.
Повний текст джерелаHussaini, Syed Abid [Verfasser]. "Complex forms of learning in honeybees: a behavioral and neural analysis & Sleep in honeybees: its role in learning and memory / Syed Abid Hussaini." Berlin : Freie Universität Berlin, 2008. http://d-nb.info/1022940260/34.
Повний текст джерелаDaniel-Weiner, Reka Verfasser], and Stefan [Akademischer Betreuer] [Pollmann. "The influence of different forms of outcome information on the neural substrates of the acquisition and representation of categories / Reka Daniel-Weiner. Betreuer: Stefan Pollmann." Magdeburg : Universitätsbibliothek, 2012. http://d-nb.info/1053914008/34.
Повний текст джерелаChaibva, Faith Anesu. "The use of response surface methodology and artificial neural networks for the establishment of a design space for a sustained release salbutamol sulphate formulation." Thesis, Rhodes University, 2010. http://hdl.handle.net/10962/d1010432.
Повний текст джерелаErpen, Luis Renato Cruz. "Reconhecimento de padrões em imagens por descritores de forma." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2004. http://hdl.handle.net/10183/27662.
Повний текст джерелаThe idea of enabling a machine to recognize the environment with which it interacts has motivated researchers to dedicate efforts in studying the most complex of the human senses: vision. Vision is essentially a task of information representation and processing, what makes it suitable for computational treatment. Given that currently there are no methods that perform equivalently to humans, the use of features has been intensively studied in order to improve the performance of the existing methods. Among these features, the shape of an object provides a powerful sign of its identity and functionality, what enables the exploitation of this feature with the purpose of recognition. This evidence distinguishes shape from other visual features, such as color, motion or texture, which, although equally important, normally do not reveal the identity of an object. As a result, the possibility of evaluating the robustness and stability of alternate techniques for shape representation is essential in order to measure the performance of each technique in the presence of uncertainty. In this work, some shape descriptors available in the literature were implemented and used in case studies aiming at evaluating their effectiveness. These case studies were carried out using characters, although, with very different purposes. The first case study is geared towards applications such as mobile robotics, where the robot recognizes commands available in the environment. The main case study is focused on applications of license plate recognition, which could be used both in situations of surveillance and traffic control and in situations of infraction. Many applications, including those that involve the search and indexing of visual objects, are suited for the use of shape features. Another important characteristic of this work is that it emphasizes that the selection of a good shape description reduces the effort during the classification step, which is computationally elevated.
Osher, David Eugene. "Function follows form : how connectivity patterns govern neural responses." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81731.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references.
Connectivity restricts and defines the information that a network can process. It is the substance of information processing that underlies the patterns of functional activity in the brain. By combining diffusion-weighted imaging or DWI, with fMRI, we are able to non-invasively measure connectivity and neural responses in the same individuals and directly relate these two measures to one another. In Chapter 2, I first establish the proof-of-principle that anatomical connectivity alone can predict neural responses in cortex, specifically of face-selectivity in the fusiform gyrus. I then extend this novel approach to the rest of the brain and test whether connectivity can accurately predict neural responses to various visual categories in Chapter 3. Finally, in Chapter 4, I compare and contrast the resulting models, which are essentially networks of connectivity that are functionally-relevant to each visual category, and demonstrate the type of knowledge that can be uncovered by directly integrating structure and function.
by David Eugene Osher.
Ph.D.in Neuroscience
Staves, Daniel Robert. "Associative CAD References in the Neutral Parametric Canonical Form." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6222.
Повний текст джерелаTomaselli, Pedro José. "Hanseníase forma neural pura: aspectos clínicos e eletroneuromiográficos dos pacientes avaliados no serviço de doenças neuromusculares do HCRP da USP no período de março de 2001 a março de 2013." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/17/17140/tde-13072014-130102/.
Повний текст джерелаBackgrounds: Leprosy is the main infectious cause of peripheral neuropathy and disabilities in the world. Its diagnosis is straightforward when the classical skin lesions are present. However, some patients present only neural involvement (pure neural form-PNL) turning its diagnosis on a great challenge. Additionally, even when this possibility is suspected, confirmation may be extremely difficult and many patients are only correctly diagnosed late on the clinical course of the disease when a severe and irreversible neuropathy is already established. Objectives: To review the characteristics of a series of PNL patients followed in our institution in the last 12 years and recognize the clinical manifestations. Methods: Inclusion criteria: Clinical evidence of peripheral nerve impairment with no skin lesions. PNL diagnose were classified as definitive when the Mycobacterium leprae was identified under nerve biopsy, and probable when a suggestive clinical picture was associated to at least one of the following: positive anti PGL1, suggestive pattern biopsy represented by the presence of epithelioid granulomatous neuritis, mononuclear cell endoneuritis and fibrose and/or an EMG pattern showing a predominantly sensory mononeuritis multiplex pattern. Exclusion criteria: Two patients were excluded because of associated diabetes mellitus, one because had CMT1A and another had HNPP. To evaluate the importance of disease duration in clinical presentation, we considered two groups according to the time course, first that disease duration of 12 or fewer months (group 1) and those with disease duration over 12 months (group 2). Results: We reviewed 34 patients with PNL, including 7 with a definite and 24 with probable diagnosis. The most common onset symptoms were sensory (91.2 %), in 70.6 % of cases symptoms started in the upper limbs, the ulnar nerve being the most frequent site. Intradermal pattern was observed only in group 1. Vibration was altered more frequent in group 2 (p=0.07), the presence of motor abnormalities (p = 0.03) and deep tendon reflexes reduced or absent in 1 or more nerves (p = 0.03) were more frequently observed in group 2. Sensory nerves most frequently involved were the ulnar and superficial peroneal. The motor nerve most often affected was the ulnar. The most frequent EMG pattern was an asymmetrical sensory and motor neuropathy with focal slowing of conduction velocity. Conclusions: PNL is an asymmetrical sensory or sensory motor neuropathy. Upper limbs are most frequent affected with frequent ulnar nerve territory involvement. Small fibers seem to be affected at early stages. Larger fibers are affected with disease progression. It is unclear whether the PNL represents a stage prior to the appearance of typical skin lesions or whether it represents a different and more aggressive leprosy type. Phenotype characterization from early signs and symptoms its a powerful tool to PNL early diagnosis.
Marangoni, André Luis 1976. "Pão de forma "zero trans" : estudo do efeito de diferentes óleos e gorduras na qualidade tecnológica dos pães." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/256010.
Повний текст джерелаTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia de Alimentos
Made available in DSpace on 2018-08-24T06:50:38Z (GMT). No. of bitstreams: 1 Marangoni_AndreLuis_D.pdf: 1214013 bytes, checksum: a252edf1c913d6c97e6f9530017bb9cd (MD5) Previous issue date: 2014
Resumo: A interesterificação é uma ferramenta fundamental para o desenvolvimento de gorduras "zero trans"; entretanto, comparada ao processo de hidrogenação, esta apresenta limitações, sobretudo no desenvolvimento de gorduras para uso em panificação. De acordo com a literatura, na produção de pão de forma, a gordura exerce diversas funções, como a lubrificação e o aumento da extensibilidade da massa, e o aumento do volume e do sabor do pão. A gordura afeta a textura, mantendo os pães macios por mais tempo; isto se deve possivelmente à sua interação com o amido da farinha, retardando o processo de retrogradação e, assim, estendendo a vida de prateleira do pão. O objetivo deste trabalho foi aplicar a tecnologia de Redes Neurais Artificiais (RNA) na formulação de gorduras "zero trans" à base de óleo de soja e gorduras interesterificadas de soja para facilitar o processo de formulação por blending, específicas para produtos de panificação, e determinar a influência das mesmas na qualidade dos pães de forma e nas interações entre as gorduras e o amido da farinha. Para tanto, foram produzidos pré-misturas e pães de forma com a adição de 4% de gordura. Como padrões, foram utilizadas gorduras comerciais, hidrogenada (GHS) e low trans (GLT), além de óleo de soja (OLS). Também foram utilizados os blends de gordura formulados através da RNA (BL1, BL2, BL3 e BL4). Para efeito de controle, foi produzido um pão sem adição de gordura (C). A análise farinográfica mostrou que a absorção de água (ABS) da farinha de trigo pura (59,0%) foi em média 6,5% maior que a das pré-misturas adicionadas de gordura. O tempo de desenvolvimento (Td) foi menor para as amostras GHS, GLT e BL4. A extensografia mostrou que, dentre todas as amostras, a BL4 foi a mais resistente (980 UE) e a menos extensível (114 mm). Isto provavelmente ocorreu devido ao menor teor de óleo de soja em sua constituição (54%), o que pode ter contribuído para uma massa de maior consistência. A análise dos pães produzidos revelou que apenas os volumes específicos das amostras OLS (3,46 mL/g) e BL4 (4,07 mL/g) diferiram significativamente entre si. A análise de firmeza dos pães mostrou que ao longo da estocagem houve diferença significativa entre a firmeza dos pães com gordura e a amostra controle (1005,75 gf), sendo este valor 13% superior ao da amostra GHS - a mais firme dentre os pães com adição de gordura. A uniformidade do miolo foi maior com a utilização de gordura. Nos pães controle (C), a porosidade (26,73%) foi quase 3 vezes superior ao das amostras com a adição dos blends. Os miolos dos pães BL1, BL2, BL3 e BL4 apresentaram alvéolos pequenos e espalhados mais uniformemente, quando comparados aos pães C, GHS, GLT e OLS. Quanto à umidade, os pães com gordura apresentaram um menor teor em relação ao da amostra controle (35%), pois as suas massas absorveram menos água durante a mistura. A análise térmica através de DSC sugeriu um efeito da gordura sobre o envelhecimento dos pães, uma vez que as variações de entalpia de retrogradação foram menores para os pães com gorduras. Os blends de gordura desenvolvidos usando a RNA e aqui utilizados, além do baixo teor de ácidos graxos trans (1,18% em média), apresentaram-se viáveis para aplicação em panificação, sobretudo o BL4
Abstract: Interesterification is a fundamental tool in the development of "zero trans" fats; however, when compared to the hydrogenation process, it presents limitations, especially when developing shortenings for bakery products. According to literature, in the production of pan bread, fat has several functions, such as lubrication and an increase in dough extensibility, and an increase in bread volume and flavor. Fat affects texture, maintaining breads soft for a longer period of time; this is possibly due to its interaction with starch in flour, retarding the retrogradation process and, thus, extending bread shelf-life. The aim of this study was to apply Artificial Neural Network (ANN) technology in the formulation of "zero trans" fats based on soybean oil and soybean interesterified fats to ease the formulation process through blending, for use in bakery products, and determine their influence on the quality of pan bread and on their interaction with starch in flour. For this, pre-mixes and breads with the addition of 4% fat were produced. As standards, commercial fats (hydrogenated soybean fat ¿ GHS and low trans fat ¿ GLT) were used, as well as soybean oil (OLS). The fat blends formulated using the ANN (BL1, BL2, BL3 e BL4) were also used. As control (C), bread without fat addition was prepared. The farinographic analysis showed that water absorption (ABS) of pure wheat flour (59.0%) was in average 6.5% higher than that of the pre-mixes of flour and fats. Dough development time (Td) was lower for the samples GHS, GLT and BL4. The extensographic analysis showed that, amongst all samples, BL4 showed the highest resistance to extension (980 EU) and the lowest extensibility (114 mm). This probably occurred due to the lower soybean oil content in its constitution (54%) that could have contributed to a more consistent dough. The analysis of the breads produced revealed that only the specific volumes of the samples OLS (3.46 mL/g) and BL4 (4.07 mL/g) differed significantly. Firmness analysis of breads showed that throughout the storage period studied there was a significant difference between the firmness of the breads with fats and the control sample (1005.75 gf), being this value 13% higher than that of GHS ¿ the firmest amongst samples with fat. Crumb uniformity was greater with the use of fat. In the control breads (C), porosity (26.73%) was almost 3 times greater than that of the samples with the addition of the blends. The crumbs of breads BL1, BL2, BL3 and BL4 presented small and more uniformly distributed alveoli, when compared to breads C, GHS, GLT and OLS. As to moisture content, breads with fat presented lower values when compared to the control sample (35%), as their doughs absorbed less water during mixing. Thermal analysis through DSC suggested an effect of fat on bread staling, once retrogradation enthalpy changes were lower for breads with fats. The fat blends developed using the ANN and used in this study, as well as having a low trans fatty acid content (1.18% in average), showed feasibility for application in pan bread, especially BL4
Doutorado
Tecnologia de Alimentos
Doutor em Tecnologia de Alimentos
Awano, Tomoyuki. "Three mutations that cause fifferent [i.e. different] forms of canine neuronal ceroid lipofuscinosis." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4592.
Повний текст джерелаTitle from title screen of research.pdf file (viewed on December 22, 2006). The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. "May 2006" Includes bibliographical references.
Книги з теми "Neural forms"
Pattern recognition and neural networks. Cambridge: Cambridge University Press, 1996.
Знайти повний текст джерелаNeural networks for pattern recognition. Oxford: Clarendon Press, 1995.
Знайти повний текст джерелаChandler, Steve. 100 formas para automotivarse. México, D.F: Diana, 2003.
Знайти повний текст джерелаservice), SpringerLink (Online, ed. Circuits in the Brain: A Model of Shape Processing in the Primary Visual Cortex. New York, NY: Springer-Verlag New York, 2009.
Знайти повний текст джерелаH, Haken, and International Workshop on "Neural and Synergetic Computers (1988 : Schloss Elmau, Bavaria), eds. Neural and synergetic computers: Proceedings of the International Symposium at Schloss Elmau, Bavaria, June 13-17, 1988. Berlin: Springer-Verlag, 1988.
Знайти повний текст джерелаSavin, Cristina, Matthieu Gilson, and Friedemann Zenke, eds. Emergent Neural Computation from the Interaction of Different Forms of Plasticity. Frontiers Media SA, 2016. http://dx.doi.org/10.3389/978-2-88919-788-0.
Повний текст джерелаFox, Kieran C. R. Neural Origins of Self-Generated Thought. Edited by Kalina Christoff and Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.1.
Повний текст джерелаDouglas, Thomas. Neural and Environmental Modulation of Motivation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198758617.003.0012.
Повний текст джерелаFox, Kieran C. R., and Manesh Girn. Neural Correlates of Self-Generated Imagery and Cognition Throughout the Sleep Cycle. Edited by Kalina Christoff and Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.16.
Повний текст джерелаMason, Peggy. Developmental Overview of Central Neuroanatomy. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190237493.003.0003.
Повний текст джерелаЧастини книг з теми "Neural forms"
Schneidereit, Toni, and Michael Breuß. "Polynomial Neural Forms Using Feedforward Neural Networks for Solving Differential Equations." In Artificial Intelligence and Soft Computing, 236–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87986-0_21.
Повний текст джерелаWang, DeLiang. "Unifying Two Forms of Memory: A Neural Model." In Computation in Neurons and Neural Systems, 281–86. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2714-5_45.
Повний текст джерелаGraham, Bruce. "Multiple Forms of Activity-Dependent Plasticity Enhance Information Transfer at a Dynamic Synapse." In Artificial Neural Networks — ICANN 2002, 45–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_8.
Повний текст джерелаTimmers, Kim, Nancy R. Voyles, Clifford King, Michael Wells, Richard Fairtile, and Lillian Recant. "Rat Islet Endocrine Cells Contain Metand Leu-Enkephalins in High- and Low-Molecular-Weight Forms." In Neural and Endocrine Peptides and Receptors, 581–98. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4684-5152-8_40.
Повний текст джерелаLiu, Lei, Zhanshan Wang, Jinhai Liu, and Zhenwei Liu. "Neural-Network-Based Adaptive Fault Estimation for a Class of Interconnected Nonlinear System with Triangular Forms." In Advances in Neural Networks – ISNN 2014, 110–20. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12436-0_13.
Повний текст джерелаNicoll, Roger A., and Robert A. Zalutsky. "Contrasting properties of NMDA-dependent and NMDA-independent forms of LTP in hippocampal pyramidal cells." In Neuroscience: From Neural Networks to Artificial Intelligence, 298–311. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78102-5_17.
Повний текст джерелаWu, Shan, Bo Chen, Xianpei Han, and Le Sun. "Data Synthesis and Iterative Refinement for Neural Semantic Parsing without Annotated Logical Forms." In Lecture Notes in Computer Science, 51–65. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18315-7_4.
Повний текст джерелаD’Argembeau, Arnaud, and Eric Salmon. "The Neural Basis of Semantic and Episodic Forms of Self-Knowledge: Insights from Functional Neuroimaging." In Advances in Experimental Medicine and Biology, 276–90. New York, NY: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1704-0_18.
Повний текст джерелаFrinken, Volkmar, Tim Peter, Andreas Fischer, Horst Bunke, Trinh-Minh-Tri Do, and Thierry Artieres. "Improved Handwriting Recognition by Combining Two Forms of Hidden Markov Models and a Recurrent Neural Network." In Computer Analysis of Images and Patterns, 189–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03767-2_23.
Повний текст джерелаStan, Cornel. "Climate-neutral fuels." In Future Fire Forms, 57–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12081-7_4.
Повний текст джерелаТези доповідей конференцій з теми "Neural forms"
Hashimoto, W. "Analysis of natural images by independent quadratic forms and temporally coherent quadratic forms." In 9th International Conference on Neural Information Processing. IEEE, 2002. http://dx.doi.org/10.1109/iconip.2002.1201929.
Повний текст джерелаPudovkin, 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.
Повний текст джерелаLi, Yifeng, Xiaodan Zhu, Richard Naud, and Pengcheng Xi. "Capsule Deep Generative Model That Forms Parse Trees." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206662.
Повний текст джерелаMurthy, Garimella Rama. "Optimization of Quadratic Forms: NP Hard Problems: Neural Networks." In 2013 International Symposium on Computational and Business Intelligence (ISCBI). IEEE, 2013. http://dx.doi.org/10.1109/iscbi.2013.51.
Повний текст джерелаMOSCOSO DEL PRADO MARTÍN, F., R. SCHREUDER, and R. H. BAAYEN. "USING THE STRUCTURE FOUND IN TIME: BUILDING DISTRIBUTED REPRESENTATIONS OF WORD FORMS BY ACCUMULATION OF EXPECTATIONS." In Proceedings of the Eighth Neural Computation and Psychology Workshop. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702784_0025.
Повний текст джерелаLiu, Lei, and Zhanshan Wang. "Adaptive fault-tolerant control for a class of uncertain nonlinear MISO discrete-time systems in triangular forms with actuator failures." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889532.
Повний текст джерелаZhao, Yunwei, Can Wang, Chi-Hung Chi, Willem-Jan van den Heuvel, Kwok-Yan Lam, and Min Shu. "Beyond the Power of Mere Repetition: Forms of Social Communication on Twitter through the Lens of Information Flows and Its Effect on Topic Evolution." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852290.
Повний текст джерелаRAUTELA, MAHINDRA, MANISH RAUT, and S. GOPALAKRISHNAN. "SIMULATION OF GUIDED WAVES FOR STRUCTURAL HEALTH MONITORING USING PHYSICS-INFORMED NEURAL NETWORKS." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36297.
Повний текст джерелаStuder, Michel, and Kara Peters. "Combined Multi-Scale Sensing and Neural Network Data Fusion for Damage Identification." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-42867.
Повний текст джерелаMcBride, Carolyn. "Genetic and neural analysis of preference for human scent in evolutionarily divergent forms of the dengue fever mosquitoAedes aegypti." In 2016 International Congress of Entomology. Entomological Society of America, 2016. http://dx.doi.org/10.1603/ice.2016.91609.
Повний текст джерелаЗвіти організацій з теми "Neural forms"
Roberts, C. D., C. J. Burden, and M. J. Thomson. Electromagnetic charged and neutral kaon form factors. Office of Scientific and Technical Information (OSTI), August 1995. http://dx.doi.org/10.2172/166447.
Повний текст джерелаBright, Stephen Taliaferro. First Measurement of Form-Factors of the Beta Decay of the Neutral Xi Hyperon. Office of Scientific and Technical Information (OSTI), January 2000. http://dx.doi.org/10.2172/1421445.
Повний текст джерелаSemerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3178.
Повний текст джерелаLuzes, Marta, and Lucina Rodríguez Guillén. La opinión pública respecto de la migración en América Latina y el Caribe. Banco Interamericano de Desarrollo, January 2023. http://dx.doi.org/10.18235/0004699.
Повний текст джерелаBragdon, Sophia, Vuong Truong, and Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/45902.
Повний текст джерелаKaufman, Lisa J. Precision Measurement of the proton neutral weak form factors at Q2 ~ 0.1 GeV2. Office of Scientific and Technical Information (OSTI), February 2007. http://dx.doi.org/10.2172/1054071.
Повний текст джерелаSchutt, Timothy C., and Manoj K. Shukla. Computational Investigation on Interactions Between Some Munitions Compounds and Humic Substances. Engineer Research and Development Center (U.S.), February 2021. http://dx.doi.org/10.21079/11681/39703.
Повний текст джерелаMoore, Winston, J. Enrique Chueca, Veronica R. Prado, Michelle Carvalho Metanias Hallack, and Laura Giles Álvarez. Energy Transition in Barbados: Opportunities for Adaptation of Energy Taxes to Mitigate Loss of Government Revenue. Inter-American Development Bank, November 2022. http://dx.doi.org/10.18235/0004534.
Повний текст джерелаNikiforov, Vladimir. Smart Technical Systems of Measuring Technology and Measuring Technique, integrated into the smart complexes of medical technologies including laser Gears with the elements of Artificial Intelligence and Artificial neural network as form of Machine Learning. Intellectual Archive, May 2019. http://dx.doi.org/10.32370/iaj.2120.
Повний текст джерелаBARKHATOV, NIKOLAY, and SERGEY REVUNOV. A software-computational neural network tool for predicting the electromagnetic state of the polar magnetosphere, taking into account the process that simulates its slow loading by the kinetic energy of the solar wind. SIB-Expertise, December 2021. http://dx.doi.org/10.12731/er0519.07122021.
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