Relevant bibliographies by topics / Machine

Academic literature on the topic 'Machine'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Machine"

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M. Brandao, Iago, and Cesar da Costa. "FAULT DIAGNOSIS OF ROTARY MACHINES USING MACHINE LEARNING." Eletrônica de Potência 27, no. 03 (September 22, 2022): 1–8. http://dx.doi.org/10.18618/rep.2022.3.0013.

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Sabeti, Behnam, Hossein Abedi Firouzjaee, Reza Fahmi, Saeid Safavi, Wenwu Wang, and Mark D. Plumbley. "Credit Risk Rating Using State Machines and Machine Learning." International Journal of Trade, Economics and Finance 11, no. 6 (December 2020): 163–68. http://dx.doi.org/10.18178/ijtef.2020.11.6.683.

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Credit risk is the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations. With the growing number of customers and expansion of businesses, it’s not possible or at least feasible for banks to assess each customer individually in order to minimize this risk. Machine learning can leverage available user data to model a behavior and automatically estimate a credit score for each customer. In this research, we propose a novel approach based on state machines to model this problem into a classical supervised machine learning task. The proposed state machine is used to convert historical user data to a credit score which generates a data-set for training supervised models. We have explored several classification models in our experiments and illustrated the effectiveness of our modeling approach.
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Nekhaev, Andrey. "Machine Wars: Machina Humeana." Sotsiologicheskoe Obozrenie / Russian Sociological Review 14, no. 3 (2015): 9–47. http://dx.doi.org/10.17323/1728-192x-2015-3-9-47.

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Fischer, Peer. "A machine from machines." Nature Physics 14, no. 11 (July 23, 2018): 1072–73. http://dx.doi.org/10.1038/s41567-018-0247-0.

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Caye, Pierre. "La machine des machines." Le Visiteur N° 27, no. 1 (March 1, 2022): 19–24. http://dx.doi.org/10.3917/visit.027.0020.

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La ville est la grande affaire de notre temps. Depuis 2008, plus de la moitié de la population mondiale vit en ville. Et nous serons probablement près de 60 % en 2030. L’attraction démographique de la ville est renforcée par son dynamisme économique. Elle est devenue le lieu central de la production et de l’innovation, le cœur du système productif et de son organisation économique. La ville contemporaine apparaît d’abord comme un mégasystème technique, le système des systèmes, l’infrastructure qui rassemble à la fois les superstructures et les autres infrastructures ou, mieux encore, la machine des machines qui interconnecte les différents réseaux structurant la vie urbaine. Mais la machine des machines n’est pas un moyen de production comme un autre, du même ordre que les machines qu’elle coordonne. La ville donne un autre sens à la notion de machine, et c’est pourquoi elle est appelée à jouer un rôle fondamental dans la transformation productive, à partir du moment du moins où est prise en compte la spécificité de sa conception et de son intelligence.
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Levi, Federico. "A machine to help machines." Nature Physics 15, no. 12 (December 2019): 1210. http://dx.doi.org/10.1038/s41567-019-0753-8.

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Trott, David. "Deceiving Machines: Sabotaging Machine Learning." CHANCE 33, no. 2 (April 2, 2020): 20–24. http://dx.doi.org/10.1080/09332480.2020.1754067.

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Engster, Frank. "Measure, machine, money." Capital & Class 44, no. 2 (February 17, 2020): 261–72. http://dx.doi.org/10.1177/0309816820904030.

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The aim of the text is to clarify why machines are economically productive only in capitalism and therefore in our society are capitalistic machines. They are capitalist not only because they increase the productive power of the capitalist valorisation, but this valorisation first of all is producing these machines, or at least it produces their productivity and hence ‘the machinic’ of machines. To understand this production of the machinic, we must understand them, as, for example, Heidegger, Simondon or Deleuze and Guattari have shown, from their context: from their non-technical essence, from their connection with other machines and from the social essence of the machinic. But in this context, first of all and in the last instance, we have to understand with Marx their entanglement with the capitalist valorisation. This can be shown for three different types of machines: the physical machine, the calculation machine and the social machine: money. What all three have in common and almost defines them as machines is that all three naturalise relations by quantifying them. The classical physical machine quantifies the relation of nature, the calculation machine quantifies information and meaning, and the money machine quantifies the relations of our society. I will concentrate on the physical and the money machine only. The technique to quantify is for both the same: measurement. This quantification and naturalisation by measurement is why both are – although or especially because they are opposed types of machines – interfaces to the capitalist valorisation process, and in this functioning as interfaces, we have to search their non-technical essence.
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Sayapin, S. N., O. O. Bryyndina, and P. G. Vanina. "New Approach to Three-Coordinate Milling of Large-Sized Surfaces of Second Order." Proceedings of Higher Educational Institutions. Маchine Building, no. 12 (741) (December 2021): 19–28. http://dx.doi.org/10.18698/0536-1044-2021-12-19-28.

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The study introduces a new approach to precision machining of large-sized surfaces of second order on a three-coordinate horizontal milling machine. The new technology does not require the use of unique large-size boring lathes or five-axis milling machines. Three-axis horizontal milling machines do not need to be equipped with additional equipment that provides the workpiece with the missing rotational and translational movements relative to the machine table. The proposed technology is based on the use of a new approach that combines the geometric parameters of the second-order machined surface, the spherical surface of the cutting tool, i.e. cutter, and their position relative to the working table. The use of the developed technology will improve the efficiency and accuracy of machining the surface of second order, as well as simplify the control program and technological support due to the absence of movable equipment. The study gives an example of practical implementation and shows the possibility of independent control of the machined parabolic surface geometry using a three-coordinate horizontal milling machine.
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Phimpisan, Phaireepinas, and Chatchapol Chungchoo. "A Best Practice Guideline for Inspecting Precision Machined Parts by Using Several Coordinate Measuring Machines (CMMs)." Applied Mechanics and Materials 894 (September 2019): 90–95. http://dx.doi.org/10.4028/www.scientific.net/amm.894.90.

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Currently in Thailand, original equipment manufacturers (OEM) who produce precision-machined parts face a serious problem about product rejection from customers. This is because measuring results from the OEM supplier differ from the measuring results from the OEM buyer. Normally, for precision-machined parts, OEM suppliers and OEM buyers use CMM machines as an inspection tool. One major cause of the difference in measurements is that suppliers and buyers use different CMM machine, operators and measuring methods. In order to mitigate the difference in measurements, the standard guideline for measuring precision-machined parts by CMM machines needs to be improved. In this research, the guideline was developed by studying the best practices of five successful companies, who could minimize rejection due to the differences in the CMM machine operators and measuring methods. Validation results indicated that when manufacturers who had precision problems used this guideline, the product rejection could decreased significantly.
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Dissertations / Theses on the topic "Machine"

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Tebbifakhr, Amirhossein. "Machine Translation For Machines." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320504.

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Traditionally, Machine Translation (MT) systems are developed by targeting fluency (i.e. output grammaticality) and adequacy (i.e. semantic equivalence with the source text) criteria that reflect the needs of human end-users. However, recent advancements in Natural Language Processing (NLP) and the introduction of NLP tools in commercial services have opened new opportunities for MT. A particularly relevant one is related to the application of NLP technologies in low-resource language settings, for which the paucity of training data reduces the possibility to train reliable services. In this specific condition, MT can come into play by enabling the so-called “translation-based” workarounds. The idea is simple: first, input texts in the low-resource language are translated into a resource-rich target language; then, the machine-translated text is processed by well-trained NLP tools in the target language; finally, the output of these downstream components is projected back to the source language. This results in a new scenario, in which the end-user of MT technology is no longer a human but another machine. We hypothesize that current MT training approaches are not the optimal ones for this setting, in which the objective is to maximize the performance of a downstream tool fed with machine-translated text rather than human comprehension. Under this hypothesis, this thesis introduces a new research paradigm, which we named “MT for machines”, addressing a number of questions that raise from this novel view of the MT problem. Are there different quality criteria for humans and machines? What makes a good translation from the machine standpoint? What are the trade-offs between the two notions of quality? How to pursue machine-oriented objectives? How to serve different downstream components with a single MT system? How to exploit knowledge transfer to operate in different language settings with a single MT system? Elaborating on these questions, this thesis: i) introduces a novel and challenging MT paradigm, ii) proposes an effective method based on Reinforcement Learning analysing its possible variants, iii) extends the proposed method to multitask and multilingual settings so as to serve different downstream applications and languages with a single MT system, iv) studies the trade-off between machine-oriented and human-oriented criteria, and v) discusses the successful application of the approach in two real-world scenarios.
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Pincumbe, Nicholas James. "Deus ex machina the God machine /." Thesis, [Tuscaloosa, Ala. : University of Alabama Libraries], 2009. http://purl.lib.ua.edu/20.

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Dinakar, Karthik. "Lensing Machines : representing perspective in machine learning." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112523.

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Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017.
Cataloged from PDF version of thesis. Due to the condition of the original material with text runs off the edges of the pages, the reproduction may have unavoidable flaws.
Includes bibliographical references (pages 167-172).
Generative models are venerated as full probabilistic models that randomly generate observable data given a set of latent variables that cannot be directly observed. They can be used to simulate values for variables in the model, allowing analysis by synthesis or model criticism, towards an iterative cycle of model specification, estimation, and critique. However, many datasets represent a combination of several viewpoints - different ways of looking at the same data that leads to various generalizations. For example, a corpus that has data generated by multiple people may be mixtures of several perspectives and can be viewed with different opinions by others. It isn't always possible to represent the viewpoints by clean separation, in advance, of examples representing each perspective and train a separate model for each point of view. In this thesis, we introduce lensing, a mixed-initiative technique to (i) extract lenses or mappings between machine-learned representations and perspectives of human experts, and (2) generate lensed models that afford multiple perspectives of the same dataset. We explore lensing of latent variable model in their configuration, parameter and evidential spaces. We apply lensing to three health applications, namely imbuing the perspectives of experts into latent variable models that analyze adolescent distress and crisis counseling.
by Karthik Dinakar.
Ph. D.
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Lanarolle, W. D. Gamini. "Machine setting automation for circular weft-knitting machines." Thesis, University of Manchester, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.488354.

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Roderus, Jens, Simon Larson, and Eric Pihl. "Hadoop scalability evaluation for machine learning algorithms on physical machines : Parallel machine learning on computing clusters." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20102.

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The amount of available data has allowed the field of machine learning to flourish. But with growing data set sizes comes an increase in algorithm execution times. Cluster computing frameworks provide tools for distributing data and processing power on several computer nodes and allows for algorithms to run in feasible time frames when data sets are large. Different cluster computing frameworks come with different trade-offs. In this thesis, the scalability of the execution time of machine learning algorithms running on the Hadoop cluster computing framework is investigated. A recent version of Hadoop and algorithms relevant in industry machine learning, namely K-means, latent Dirichlet allocation and naive Bayes are used in the experiments. This paper provides valuable information to anyone choosing between different cluster computing frameworks. The results show everything from moderate scalability to no scalability at all. These results indicate that Hadoop as a framework may have serious restrictions in how well tasks are actually parallelized. Possible scalability improvements could be achieved by modifying the machine learning library algorithms or by Hadoop parameter tuning.
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Kent, W. F. "Machine learning for parameter identification of electric induction machines." Thesis, University of Liverpool, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399178.

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This thesis is concerned with the application of simulated evolution (SE) to the steady-state parameter identification problem of a simulated and real 3-phase induction machine, over the no-load direct-on-line start period. In the case of the simulated 3-phase induction machine, the Kron's two-axis dynamic mathematical model was used to generate the real and simulated system responses where the induction machine parameters remain constant over the entire range of slip. The model was used in the actual value as well as the per-unit system, and the parameters were estimated using both the genetic algorithm (GA) and the evolutionary programming (EP) from the machine's dynamic response to a direct-on-line start. Two measurement vectors represented the dynamic responses and all the parameter identification processes were subject to five different levels of measurement noise. For the case of the real 3-phase induction machine, the real system responses were generated by the real 3-phase induction machine whilst the simulated system responses were generated by the Kron's model. However, the real induction machine's parameters are not constant over the range of slip, because of the nonlinearities caused by the skin effect and saturation. Therefore, the parameter identification of a real3-phase induction machine, using EP from the machine's dynamic response to a direct-on-line start, was not possible by applying the same methodology used for estimating the parameters of the simulated, constant parameters, 3-phase induction machine.
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Sokola, Matija. "Vector control of induction machines using improved machine models." Thesis, Liverpool John Moores University, 1998. http://researchonline.ljmu.ac.uk/4899/.

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El, Fawal Ahmad Hani. "Machine-to-machine communication congestion mechanism." Thesis, Brest, École nationale supérieure de techniques avancées Bretagne, 2018. http://www.theses.fr/2018ENTA0010/document.

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Nos travaux s’orientent vers les problèmes de réseaux sans fil liés à la coexistence des communications machine-to-machine (M2M) et humain-humain (H2H). On souhaite souligner l'impact mutuel entre les trafics M2M et H2H dans un contexte d’Internet des objets (IoT : Internet Of Things) en particulier lors des catastrophes. Les communications M2M, qui devraient connaître une roissance exponentielle dans un avenir proche, constitueront un facteur important pour affecter tous les réseaux mobiles. On prévoit un grand nombre d'appareils M2M qui entraînera inévitablement des problèmes de saturation et aura des impacts remarquables sur les trafics, les services et les applications M2M et H2H. Pour étudier les influences mutuelles M2M et H2H, nous développons un nouveau modèle markovien à temps continu (CTMC) pour simuler, analyser et mesurer les différentes stratégies d'accès aux réseaux sans fil. Notre modèle nous a permis de contourner certaines limitations des simulateurs professionnels de LTE-A (Long Term Evolution-Advanced) comme SimuLTE en terme d’un nombre massif d'appareils M2M, une flexibilité de certains paramètres ou pour élaborer plus des outils statistiques. Lors d’un sinistre et suite à un énorme nombre de M2M souhaitant accéder aux réseaux sans-fil, nous avons constaté un épuisement rapide de la bande passante allouée dans les réseaux LTE-M (Long Term Evolution for Machines) ou Narrow Band for IoT (NB-IoT). Pour résoudre ce problème, nous proposons une nouvelle approche appelée Adaptive eNodeB (A-eNB) pour les réseaux LTE-M et NB-IoT. Selon nos simulations, l’A-eNB peut résoudre progressivement le problème de surcharge tout en assurant une satisfaisante qualité de service (QoS) pour le trafic H2H. Avec le concept d’A-eNB, un réseau LTE-M pourra adapter ses ressources pour faire face à une augmentation progressive du nombre de connexions M2M accédant au réseau LTE-M / NB-IoT et en même temps réduire l'impact sur le trafic H2H
This Ph.D. work aims to study the Machine-to-Machine (M2M) congestion overload problem and the mutual impact among M2M and Human-to-Human (H2H) traffics in IoT (Internet of Things) environments specifically during disaster events. M2M devices with their expected exponential booming in the near future, will be one of the significant factors to influence all mobile networks. Inevitably, the expected huge number of M2M devices causes saturation problems, and leads to remarkable impacts on both M2M and H2H traffics, services and applications. To study the M2M and H2H mutual influences, we create a new platform model based on Continuous-Time Markov Chain (CTMC) to simulate, analyze and measure radio access strategies due to the limitations of existing Long Term Evolution-Advanced (LTE-A) simulators (i.e, SimuLTE) in term of massive M2M devices, parameter flexibility and statistical tools. Additionally, during disaster events, a fast bandwidth depletion of the limited bandwidth assigned to M2M devices in Long Term Evolution for Machines (LTE-M) and Narrow Band for IoT (NB-IoT) networks is expected due to the high arrival request of M2M device network access. To address this problem, we propose a new approach named Adaptive eNodeB (A-eNB) for both LTE-M and NB-IoT networks. The A-eNB can solve gradually the overload problem, while keeping the H2H traffic Quality of Service (QoS) not to be affected badly. The network adaptation is provided through a dynamic LTE-M resource reservation aiming to increase the number of M2M connections accessing the LTE-M/NB-IoT network and to decrease the impact on H2H traffic
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Wright, David N. "Machine." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ47767.pdf.

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Hack, Byron Wallis John 1963. "Man to machine, machine to machine and machine to instrument interfaces for teleoperation of a fluid handling laboratory." Thesis, The University of Arizona, 1988. http://hdl.handle.net/10150/276764.

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The purpose of this thesis is the design of the software necessary for teleoperation of a fluid handling laboratory. It does not include the implementation of this software. The laboratory for which it is designed is being developed at the University of Arizona, and is a model of the fluid handling laboratory aboard Space Station. The software includes man/machine, machine/machine, and machine/instrument interfaces. The man/machine interface is menu driven and consists of high level commands which are independent of the devices in the laboratory. The machine/machine interface is also device independent. It consists of intermediary commands and maps the commands of the man/machine interface into the low level, device dependent, commands and programs of the machine/instrument interface. Although the software is primarily designed for the model laboratory, the needs of a remotely operated fluid handling laboratory aboard Space Station have been considered.
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Books on the topic "Machine"

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Iannucci, Robert A. Parallel Machines: Parallel Machine Languages. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-1543-8.

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David, Barlam, and Nystrom Frederic E, eds. Machine elements: Life and design. Boca Raton, FL: CRC Press, 2008.

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Katharina, Dohm, Stahlhut Heinz, Schirn Kunsthalle Frankfurt, and Museum Jean Tinguely Basel, eds. Kunstmaschinen Maschinenkunst =: Art machines Machine art. Heidelberg: Kehrer, 2007.

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Schie, Hidde van, 1978- writer of supplementary textual content, ed. Idiosyncratic machine: Idiosyncratische machine. [Sint-Amandsberg]: Art Paper Editions, 2019.

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Esposito, Anthony. Machine design. 2nd ed. New York: Delmar Publishing, 1991.

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Narayana, K. L. Machine drawing. 3rd ed. New Delhi: New Age International (P) Ltd., Publishers, 2006.

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Esposito, Anthony. Machine design. 2nd ed. Albany, N.Y: Delmar Publishers, 1991.

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Adolphsen, Peter. Machine. San Francisco, Calif: MacAdam/Cage, 2007.

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Adolphsen, Peter. Machine. København: Samleren, 2006.

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Hamill, Denis. Machine. London: Sphere, 1986.

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Book chapters on the topic "Machine"

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Bringsjord, Selmer, Naveen Sundar Govindarajulu, Shreya Banerjee, and John Hummel. "Do Machine-Learning Machines Learn?" In Studies in Applied Philosophy, Epistemology and Rational Ethics, 136–57. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96448-5_14.

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Bourgeau, Thomas, Hakima Chaouchi, and Pinar Kirci. "Machine-to-Machine Communications." In Next-Generation Wireless Technologies, 221–41. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5164-7_11.

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Sathi, Arvind. "Machine-to-Machine Interfaces." In Cognitive (Internet of) Things, 125–36. New York: Palgrave Macmillan US, 2016. http://dx.doi.org/10.1057/978-1-137-59466-2_9.

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Knoll, Thomas, Alexander Lautz, and Nicolas Deuß. "Machine-To-Machine Communication." In Handbuch Industrie 4.0, 573–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-58530-6_84.

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Snider, Alvin. "Machine." In Encyclopedia of Renaissance Philosophy, 1–10. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-02848-4_942-1.

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Weik, Martin H. "machine." In Computer Science and Communications Dictionary, 949. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_10798.

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Heßler, Martina. "Machine." In Handbook of the Anthropocene, 957–62. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25910-4_157.

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Javed, Adeel. "IoT Patterns: Machine to Machine." In Building Arduino Projects for the Internet of Things, 241–51. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1940-9_11.

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Rendell, Paul. "Universal Counter Machine—Turing Machine." In Turing Machine Universality of the Game of Life, 143–46. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19842-2_9.

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Les, Zbigniew, and Magdalena Les. "Machine Perception—Machine Perception MU." In Machine Understanding, 9–44. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24070-7_2.

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Conference papers on the topic "Machine"

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Al-ani, M. M. J., S. P. Lee, and J. M. Allport. "Integrated Electrical Machine-Turbo Machinery." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-63429.

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Global warming and climate change due to rising levels of greenhouse gases have placed significant pressure on the automobile industry to adopt more clean fuel, transportation electrification, and waste energy recovery technologies. As a result, several electrically assisted or driven turbo-machines have been proposed such as turbochargers, turbo-compressors, and electrical boosters. Therefore, the electrification of turbo-machinery has been trending for the past decade. All the systems mentioned above consist of a conventional electrical machine connected to the turbo-machinery by a shaft, making the system relatively large, heavy, costly and mechanically complex. In this paper, an integrated electrical machine-turbo machinery concept is proposed. It consists of an electrical machine mounted around a salient rotor that is shaped like an axial flow turbo-machinery wheel. The electrical machine can be used as a motor to drive or assist the operation of the turbo-machine or it can operate as a generator powered by the gas or fluid flow. Compared to the conventional electrically derived or assisted turbo-machine systems, the proposed system is expected to decrease the overall size, weight and complexity. In order to illustrate this concept, an initial multi-physics feasibility study is presented. Electromagnetic and mechanical, performance are calculated and investigated using FEA. Additionally, aerodynamic consideration has been illustrated.
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Bojic, Iva, and Mario Kusek. "Self-synchronization of Nonidentical Machines in Machine-to-Machine Systems." In 2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems (SASO). IEEE, 2013. http://dx.doi.org/10.1109/saso.2013.39.

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Wang, Sunran, and Hongyu Di. "Machine intelligence and intelligent machines." In 2011 International Conference on Fluid Power and Mechatronics (FPM). IEEE, 2011. http://dx.doi.org/10.1109/fpm.2011.6045814.

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Bacon, David F. "Session details: Machine machinery." In OOPSLA05: ACM SIGPLAN Object Oriented Programming Systems and Applications Conference. New York, NY, USA: ACM, 2005. http://dx.doi.org/10.1145/3244455.

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Rasnic, Russ, and Joe A. Capps. "Machine Guarding, Lockout/Tagout, and the Interlocked Guard." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-60445.

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Unguarded or poorly guarded machinery claims many limbs and lives each year. Similarly, injuries from machinery undergoing maintenance and/or cleaning operations are far too numerous. Machine guarding and related machinery violations continue to rank in the top 10 in OSHA citations. The lists of types of machines and their associated hazards, as well as the types of injuries are too lengthy to mention. Maximizing the effectiveness of safeguards is essential for protecting workers from preventable injuries. One of the most effective means of machinery guarding is the interlocking guard, which is a device that shuts off or disables a machine function when a guard or cover is removed or opened. Interlocking guards are an important component of guarding safety. Often, an interlocked guard is the only type of guard that can be used. This paper presents several scenarios where the interlocking guard was the best available technology for handling the hazard. Interlocks are a rapidly developing field. The advantages and drawbacks of interlocks are discussed, as well as the different types of interlocks available.
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Otsubo, Tatsuki, Takanori Yazawa, Jinhui Wang, and Tomonori Kato. "Diamond Fly Cutting Applied to Improve Curved Surface Machining by In-Process Measurement and Control on an Ordinary Milling Machine." In JSME 2020 Conference on Leading Edge Manufacturing/Materials and Processing. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/lemp2020-8590.

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Abstract To improve the accuracy of the machined surface produced by an ordinary milling machine, a system called workpiece-referred form accuracy control (WORFAC) was developed and confirmed in diamond turning. However, non-rotational symmetric surface structures, such as V-grooves, pyramid structures, F-theta lenses, and other free form surface cannot be machined by diamond turning. We proposed to improve the form accuracy of a machined surface produced by an ordinary milling machine by diamond fly cutting using controlled cutting with reference surface (CCRS), an in-process measurement and control method. Fly cutting is usually used to manufacture ultra-precision microstructures with nanometric surface roughness and submicrometric form accuracy, without the need for subsequent polishing. Nevertheless, a high level of accuracy has only recently been achieved on ultra-precision milling machines. In this study, we verified the effectiveness of fly cutting with CCRS on an ordinary milling machine. CCRS improves machined surface accuracy by controlling the relative displacement between the tool and workpiece. Diamond fly cutting using CCRS was demonstrated to reduce the table motion error on an ordinary milling machine. The experiments of curved surface machining by uncontrolled machining and control machining were conducted, and the effectiveness of improving the circular are machining accuracy of the general-purpose milling machine was confirmed.
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Baek, Ji Min, Kyeong Ha Lee, Seung Ho Lee, and Ja Choon Koo. "Cost Effective On-Site Fault Diagnosis Home Appliance Using a Smart Phone and Support Vector Machine." In ASME 2019 28th Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/isps2019-7445.

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Abstract One of the common rotating machines of the consumer electronics might be a washing machine. The rotating machinery normally suffers mechanical failures even during daily operations that results in poor performance or shortening lifetime of the machine. Therefore, engineers have been interested in the earliest fault diagnosis of the rotating machine. Existing fault diagnosis methods for rotating machines have used fast fourier transform (FFT) method in frequency domain to detect abnormal frequency. However, it is difficult to diagnose using the FFT method if the normal frequency components of the rotating machines overlaps with the fault frequencies. In this paper, sets of acoustic signals generated by the washing machines are collected by using a smart phone in which an inexpensive microphone is equipped, and collected data are analyzed using a new algorithm, which combining the skewness, kurtosis, A-weighting filter, high-pass filter (HPF), and FFT. The analyzed data is applied to support vector machine (SVM) to determine defect existence. The proposed algorithm solves the disadvantages of the existing method and is accurate enough to discriminate the data collected by the cheap microphone of the smart phone.
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Kozhenkov, A., E. Z. Naeini, and K. Prindle. "Machine Learning and Learning from Machines." In Progress’19. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201953052.

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Centner, Matthias. "Machine design software for induction machines." In 2008 International Conference on Electrical Machines (ICEM). IEEE, 2008. http://dx.doi.org/10.1109/icelmach.2008.4800202.

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Bernard, Catherine. "Flesh Machine - Vision Machine - War Machine." In Politics of the Machines - Art and After. BCS Learning & Development, 2018. http://dx.doi.org/10.14236/ewic/evac18.38.

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Reports on the topic "Machine"

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Vesselinov, Velimir Valentinov. Machine Learning. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1492563.

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Valiant, L. G. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada283386.

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Loscalzo, Steven, Nathaniel Gmelli, and Robert Wright. Machine Intelligence. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada580353.

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Angrist, Joshua, and Brigham Frandsen. Machine Labor. Cambridge, MA: National Bureau of Economic Research, December 2019. http://dx.doi.org/10.3386/w26584.

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Chase, Melissa P. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, April 1990. http://dx.doi.org/10.21236/ada223732.

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Klarer, P. R. Flocking small smart machines: An experiment in cooperative, multi-machine control. Office of Scientific and Technical Information (OSTI), March 1998. http://dx.doi.org/10.2172/573344.

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Wei, Jie. AGS Machine Studies. Office of Scientific and Technical Information (OSTI), October 1994. http://dx.doi.org/10.2172/1119436.

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Stoner, William W. Adaptive Machine Vision. Fort Belvoir, VA: Defense Technical Information Center, January 1989. http://dx.doi.org/10.21236/ada208130.

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Boscher, C., P. Cheval, L. Wu, and E. Gray. LDP State Machine. RFC Editor, January 2002. http://dx.doi.org/10.17487/rfc3215.

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Stoner, William W., Michael H. Brill, and Doreen W. Bergeron. Adaptive Machine Vision. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada197039.

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