Academic literature on the topic 'Flowout'
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Journal articles on the topic "Flowout"
Riemann, Jan, Martin Schmitz, Alexander Hendrich, and Max Mühlhäuser. "FlowPut." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, no. 1 (March 26, 2018): 1–23. http://dx.doi.org/10.1145/3191763.
Full textEzeofor, Victory ‘Segun, Nathan Bray, Lucy Bryning, Farina Hashmi, Henrik Hoel, Daniel Parker, and Rhiannon Tudor Edwards. "Economic model to examine the cost-effectiveness of FlowOx home therapy compared to standard care in patients with peripheral artery disease." PLOS ONE 16, no. 1 (January 14, 2021): e0244851. http://dx.doi.org/10.1371/journal.pone.0244851.
Full textGuo, Zhiqiang, Xiaodong Dong, Sheng Chen, Xiaobo Zhou, and Keqiu Li. "EasyLB: Adaptive Load Balancing Based on Flowlet Switching for Wireless Sensor Networks." Sensors 18, no. 9 (September 12, 2018): 3060. http://dx.doi.org/10.3390/s18093060.
Full textQiao, H. J., X. Wan, J. Z. Xu, S. Y. Li, and P. P. He. "DEEP LEARNING BASED OPTICAL FLOW ESTIMATION FOR CHANGE DETECTION: A CASE STUDY IN INDONESIA EARTHQUAKE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 317–22. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-317-2020.
Full textGooding, Mark. "Flowol (Data Harvest)." Electronics Education 2002, no. 2 (2002): 33. http://dx.doi.org/10.1049/ee.2002.0028.
Full textAssche, Dimitri Van, and Yves De Smet. "FlowSort parameters elicitation based on categorisation examples." International Journal of Multicriteria Decision Making 6, no. 3 (2016): 191. http://dx.doi.org/10.1504/ijmcdm.2016.079712.
Full textDe Smet, Yves, and Dimitri Van Assche. "FlowSort parameters elicitation based on categorisation examples." International Journal of Multicriteria Decision Making 6, no. 3 (2016): 191. http://dx.doi.org/10.1504/ijmcdm.2016.10000527.
Full textSera, M., and T. Kanda. "Measurement of River Flows with Ultrasonic Flowmet." PROCEEDINGS OF THE JAPANESE CONFERENCE ON HYDRAULICS 32 (1988): 353–58. http://dx.doi.org/10.2208/prohe1975.32.353.
Full textBotta, Cirino, Catarina Maia, Cristina Pérez Ruiz, Irene Manrique, Juan José Garcés, Sara Rodríguez, Leire Burgos, et al. "Flowct: A Semi-Automated Workflow for Deconvolution of Immunophenotypic Data and Objective Reporting on Large Datasets." Blood 134, Supplement_1 (November 13, 2019): 4355. http://dx.doi.org/10.1182/blood-2019-122088.
Full textArrillaga, J., N. R. Watson, and G. N. Bathurst. "A Multifrequency Power Flowof General Applicability." IEEE Transactions on Power Delivery 19, no. 1 (January 2004): 342–49. http://dx.doi.org/10.1109/tpwrd.2003.820193.
Full textDissertations / Theses on the topic "Flowout"
Bean, Ian James Materials Science & Engineering Faculty of Science UNSW. "Blast furnace hearth drainage improvement of the residual - flowout correlation." Publisher:University of New South Wales. Materials Science & Engineering, 2008. http://handle.unsw.edu.au/1959.4/41490.
Full textHautala, M. (Mikko). "Modeling of electrolytic solutions and implementation of the models in Flowbat." Master's thesis, University of Oulu, 2016. http://urn.fi/URN:NBN:fi:oulu-201605041611.
Full textTämän työn tarkoituksena oli luoda Flowbat ohjelma jolla pystytään laskemaan elektrolyyttiliuoksissa olevien komponenttien aktiivisuuskertoimet. Näissä liuoksissa elektrolyytit ovat dissosioituneet ioneiksi, jotka pienissäkin pitoisuuksissa kasvattavat liuoksen epäideaalista käyttäytymistä suuresti. Elektrolyyttiliuosten käyttäytymisen mallintaminen on keskeisessä osassa etenkin erilaisten bioprosessien tarkastelussa, koska näissä prosesseissa on tyypillisesti useita elektrolyyttejä seoksessa. Tämä erittäin epäideaalinen käyttäytyminen täytyy huomioida tarkasti aktiivisuuskertoimia laskettaessa, joka myös vastaavasti vaikuttaa systeemin ominaisuuksiin faasi- ja reaktiotasapainossa. Tämän työn teoriaosa alkaa elektrolyytti- ja ei-elektrolyyttiliuosten kaasu–neste-tasapainolaskentaan käytettävien menetelmien esittelyllä. Näitä menetelmiä kutsutaan ”fii-fii” ja ”gamma-fii” lähestymistavoiksi. Seuraavaksi esitellään eri vaihtoehdot elektrolyyttiliuosten fugasiteettikertoimien ja aktiivisuuskertoimien arviointia varten. Fugasiteettikertoimet voidaan laskea erilaisilla tilanyhtälöillä, kun taas aktiivisuuskertoimet voidaan laskea aktiivisuuskerroin mallien avulla. Valinta näiden mallien välillä ei ole suoraviivaista, mutta yleisimmissä tapauksissa sovelletaan aktiivisuuskerroin malleja. Tästä syystä erinäisiä aktiivisuuskerroin malleja, kuten eNRTL ja LIQUAC, tullaan käsittelemään enimmäkseen tässä työssä. Ionien huomioiminen liuoksessa edellyttää myös eri standarditilojen käyttöä riippuen tarkasteltavasta kohteesta. On siis tarpeellista myös esitellä laajimmin käytetyt standarditilat yleisesti sekä valintaperusteita niiden valinnalle eri seoksilla. Johtuen ionien vaikutuksista, liuoksen käyttäytyminen on riippuvainen eri osaslajien välisistä sekä pitkän kantaman että lyhyen kantaman vuorovaikutuksista. Lyhyen kantaman vuorovaikutuksia voidaan mallintaa perinteisillä ei-elektrolyyteille muodostetuilla aktiivisuuskerroinmalleilla, kun taas pitkän kantaman vuorovaikutuksia täytyy mallintaa eri tyyppisillä malleilla. Ionien aiheuttamat elektrostaattiset voimat ovat pitkän kantaman vuorovaikutuksia. Näitä ilmiöitä pystytään mallintamaan Poisson-Boltzmannin teorian ja Bornin mallin avulla, sekä niiden vaikutus aktiivisuuskertoimiin voidaan huomioida Debye-Hückel ja MSA (mean spherical approximation) teorioiden avulla. Koska suurin osa aktiivisuuskerroinmalleista hyödyntää Debye-Hückel teoriaa pitkän kantaman Coulombisten vuorovaikutusten mallintamiseen, sitä käsitelleään erityisen tarkasti tämän työn teoriaosassa. Työn soveltavassa osuudessa symmetrinen eNRTL aktiivisuuskerroinmalli valittiin perusteellista tarkastelua varten ja tämä malli ohjelmoitiin Flowbat-simulointiohjelmistoon käyttäen Fortran-ohjelmointikieltä. Tämän luodun Flowbat- ohjelman tulosten paikkansapitävyyttä arvioitiin vertaamalla ohjelmalla laskettuja tuloksia Aspen Plus -simulointiohjelmiston käytännössä identtisen ENRTL-SR-mallin laskentatuloksiin. Nämä testit suoritettiin seuraavilla seoksilla: vesi + 1-propanoli + NaCl, vesi + 1-propanoli + NaCl + CaCl₂, vesi + muurahaishappo, vesi + ammoniakki + rikkivety. Erot tässä työssä muodostetun Flowbat-ohjelman ja Aspen Plus -ohjelman laskemissa aktiivisuuskertoimissa osoittautuivat suhteellisen vähäisiksi. Nämä pienet erot johtuvat todennäköisesti käytettyjen ohjelmistojen perustavanlaatuisista eroavaisuuksista. Täten tämän työn varsinainen päämäärä saavutettiin onnistuneesti. Muodostetun Flowbat-ohjelman ja Aspen Plus simulointien luomisen välivaiheet on dokumentoitu työssä yksityiskohtaisesti, joten muodostettua Flowbat-ohjelmaa voidaan tulevaisuudessa käyttää elektrolyyttimallien soveltamiseen erilaisiin prosessisuunnittelu- ja -analyysitarkasteluihin
Houston, Jonas H. "Developing Dendrifrom Facades Using Flow Nets as a Design Aid." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/670.
Full textVan, Assche Dimitri. "New methodological perspectives on PROMETHEE methods." Doctoral thesis, Universite Libre de Bruxelles, 2019. https://dipot.ulb.ac.be/dspace/bitstream/2013/287858/6/contratDV.pdf.
Full textDoctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
Medeiros, Junior Flavio. "Comportamento da temperatura do fluido infetado no reservatorio em operações de aciclificação de matriz." [s.n.], 1996. http://repositorio.unicamp.br/jspui/handle/REPOSIP/264312.
Full textDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica
Made available in DSpace on 2018-07-21T20:38:31Z (GMT). No. of bitstreams: 1 MedeirosJunior_Flavio_M.pdf: 5849713 bytes, checksum: d198866e74ce5c49813b449117b5005b (MD5) Previous issue date: 1996
Resumo: As operações de Acidificação de Matriz são usadas com freqüência para remoção do dano à formação causado por partículas sólidas de minerais que obstruem parcialmente o espaço poroso nas proximidades do poço. A cinética da reação química entre o ácido e os minerais da rocha é bastante sensível à temperatura na qual a reação se desenvolve. A temperatura do ácido na zona injetora depende dos fenômenos de transferência de calor que ocorrem no interior da coluna de injeção e no reservatório submetido à injeção do ácido. Normalmente utiliza-se pequenos volumes de ácidos bombeados com vazões médias e altas tendo como conseqüência pequenos períodos de injeção. O modelo proposto para o cálculo da temperatura na coluna considera os termos de acumulação de calor no interior da coluna e no anular, além da hipótese de poço com raio finito, importantes para soluções de curto tempo. No reservatório são consideradas as perdas para a formação vizinha e analisada a influência do calor gerado pela reação química no comportamento da temperatura do ácido. As hipóteses de condução de calor desprezível na direção do fluxo de fluido, equilíbrio térmico instantâneo, gradiente geotérmico linear e condução de calor unidimensional na formação também são utilizadas. A solução para temperatura do ácido na zona. injetora é apresentada no espaço de Laplace considerando o acoplamento da solução para temperatura na coluna à solução para a temperatura no reservatório através do teorema da convolução. A inversão numérica das funções no espaço de Laplace é feita com o algoritmo de Stehfest. Os resultados obtidos indicam que a temperatura do ácido na zona injetara é bastante sensível à vazão do ácido injetado e aos volumes deslocados a frente do tratamento, podendo atingir valores significativamente inferiores à temperatura do reservatório no decorrer da operação
Abstract: Matrix Acidization is frequently used to remove formation damage from pore plugging caused by deposition of small mineral particles. The kinetics of the chemical reactions between acid and minerais is very sensitive to fluid temperature. The temperature of the acid at the injection zone depth depends on the heat transfer that occurs as the acid travels through the injection column and on the heat exchange with the reservoir rock and fluids. The volume of injected acid is usually small and the injection time is short, compared to other thermal injection operations. Two heat transfer models are proposed: one to calculate the temperature profile along the injection column and other to calculate the temperature distribution within the reservoir. The model proposed here for the heat transfer along the column incorporates heat accumulation (in the fluid and at the annulus) to the heat exchange between the wellbore and the semi-infinite surroundings. For the heat transfer in the reservoir, the modei accounts for the conductive losses to the neighboring formation, the heat generation from the chemical reactions and heat transported by the injected fluid. The eventual temperature distribution for the acid in the reservoir is obtained after coupling of the solution for the column problem and the solution for the reservoir problem. Both problems were solved by Laplace transforms and the final solution is obtained using the convolution theorem. Stehfest's algorithm is used to invert the solution to real domain. Results for typical operational parameters show that the acid temperature is very sensitive to the injection flow rate and to the fluid volumes injected during the pre-treatment. The acid temperature can be significantly lower than the reservoir temperature along the operation
Mestrado
Mestre em Engenharia de Petróleo
Borges, Edilso Macedo Lopes. "Avaliação de Correlações e Equações de Estado para Determinação de Fatores de Compressibilidade de Gás Natural." Universidade do Estado do Rio de Janeiro, 2009. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=2574.
Full textThe compressibility factor (Z-factor) of natural gases is necessary in many petroleum engineering calculations (evaluation of formation, pressure drop through a pipe, pressure gradient in gas wells, material balance calculations, gas metering, gas compression and processing). The most common sources of Z-factor values are experimental measurements, which are expensive and time consuming. This property is also estimated from empirical correlations, corresponding state models or equations of state (EOS) when experimental data is unavailable. Capabilities of Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), Patel-Teja (PT), Patel-Teja-Valderrama (PTV), Schmidt-Wenzel (SW), Lawal-Lake-Silberberg (LLS) and AGA-8 to predict this property of 2200 data points under various schemes of binary interaction numbers are thoroughly investigated. This database was split on four groups: Group 1 (presence of hydrocarbon plus fraction - C7+), Group 2 (temperatures lower than 258.15 K), Group 3 (pressures higher than 10000 kPa) e Group 4 (pressures lower than 10000 kPa). The best results were obtained from EOS AGA-8 for pressures up to 70000 kPa. However, an increasing trend in average absolute deviations was observed as a function of CO2 e H2S concentrations. EOS PTV and EOS SW are capable to predict the compressibility factor (Z) with average absolute deviation between calculated and experimental values with satisfactory accuracy for most applications for a wide range of temperature and pressure. This study also presents an evaluation of 224 possible methods of calculating the gas compressibility factor, based on 8 correlations and corresponding state models, combined with 4 mixing rule that predict the pseudo-reduced pressure and temperatures of the mixture, that were combined with 7 methods of characterizing the plus fraction critical properties when present in the gas composition. Results suggest for different systems conditions, the best correlation and mixing rule combination capable of predicting Z-factor with the least average absolute relative error
Rovenský, Vladimír. "Workflow Modelling." Master's thesis, 2011. http://www.nusl.cz/ntk/nusl-313755.
Full text謝啟郎. "On the flowof powder material in a laboratory scale screwfeeder." Thesis, 1991. http://ndltd.ncl.edu.tw/handle/55307415993265045244.
Full textSkalický, Tomáš. "Interactive scheduling and visualisation." Master's thesis, 2012. http://www.nusl.cz/ntk/nusl-313840.
Full textLin, Yu-Jie, and 林煜傑. "MODFIM: Mitigating Real-Time Datacenter Network Microbursts by Orderly Detour with Flowlet Intervals." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/v6b3c3.
Full text國立交通大學
電機工程學系
108
Microbursts in the datacenter network (DCN) last for a very short period in switches and are difficult to be discovered from a course-grained perspective; Some previous works are dedicated to microburst detection in the network and have not yet tried to mitigate microbursts. Therefore, in this paper, a real-time microburst mitigator, MODFIM, is proposed and utilizes orderly detour with flowlet intervals to reduce packet loss in an in-network manner. MODFIM consists of three major components: (1) Detour Launcher, (2) Packet Sequencer and (3) Post-detour Handler. With the aid of programmable switches, our approach can entirely work in the network. To the best of our knowledge, MODFIM is also the first solution capable of mitigating microbursts in real time, meanwhile achieving zero loss and minimizing packet reordering.
Books on the topic "Flowout"
Limited, Property Market Research Services. Shopping flowcount. [S.l.]: Property Market Research Services Limited, 1994.
Find full textProperty Market Research Services Limited. Shopping flowcount: Oxford. London: Property Market ResearchServices Limited, 1991.
Find full textProperty Market Research Services Limited. Shopping flowcount: Oxford. London: Property Market Research Services Limited., 1989.
Find full textProperty Market Research Services Limited. Shopping flowcount: Oxford. London: Property Market Research Services Limited, 1994.
Find full textProperty Market Research Services Limited. Banbury: 1996 pedestrian flowcount. London: Property Market Research Services Limited, 1996.
Find full textChi, S. Chang. The applications of flowcut grouting curtain in mine water preventing controlling. S.l: s.n, 1985.
Find full textProperty Market Research Services Limited. Shopping flowcount: An aid to shop property evaluation for retailers, investors and agents. London: Property Market research Services Limited, 1994.
Find full textShopping flowcount. [s.l.]: Property Market Research Services, 1985.
Find full textShopping flowcount: Banbury. [s.l.]: Property Market Research Services, 1986.
Find full text(Illustrator), Richard Chasemore, ed. Control and Monitoring with Flowol2 (Basic ICT Skills). Payne-Gallway Publishers, 2003.
Find full textBook chapters on the topic "Flowout"
Lee, Chankyu, Adarsh Kumar Kosta, Alex Zihao Zhu, Kenneth Chaney, Kostas Daniilidis, and Kaushik Roy. "Spike-FlowNet: Event-Based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks." In Computer Vision – ECCV 2020, 366–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58526-6_22.
Full textXie, Xuemei, Shu Zhang, Jinjian Wu, Xun Xu, Guangming Shi, and Jianyu Chen. "A Real-Time Rock-Paper-Scissor Hand Gesture Recognition System Based on FlowNet and Event Camera." In Pattern Recognition and Computer Vision, 98–109. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31654-9_9.
Full textSchanz, Daniel, Andreas Schröder, Sebastian Gesemann, Florian Huhn, Matteo Novara, Reinhard Geisler, Peter Manovski, and Karthik Depuru-Mohan. "Recent Advances in Volumetric Flow Measurements: High-Density Particle Tracking (‘Shake-The-Box’) with Navier-Stokes Regularized Interpolation (‘FlowFit’)." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 587–97. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64519-3_53.
Full text"Cut-Ups and Glitches: The Freeze and Flowof Los Sampler’s and Squarepusher." In Digital Signatures. The MIT Press, 2016. http://dx.doi.org/10.7551/mitpress/10192.003.0006.
Full textRaudaskoski, R., E. Turpeinen, H. Turunen, K. Rahkamaa-Tolonen, and R. L. Keiski. "Vapour pressure of CO2 as well as phase envelopes and critical points for CO2 and CH4 mixtures calculated by Aspen Plus and Flowbat simulation software." In Carbon Dioxide Utilization for Global Sustainability, Proceedings of 7ththe International Conference on Carbon Dioxide Utilization, 393–96. Elsevier, 2004. http://dx.doi.org/10.1016/s0167-2991(04)80285-4.
Full textConference papers on the topic "Flowout"
Dhamdhere, Amogh, Lee Breslau, Nicholas Duffield, Cheng Ee, Alexandre Gerber, Carsten Lund, and Subhabrata Sen. "Flowroute." In the 10th annual conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1879141.1879183.
Full textAlim, Abdul, Mehdi Bezahaf, and Laurent Mathy. "FlowOS." In the eighth ACM/IEEE symposium. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2396556.2396588.
Full textBezahaf, Mehdi, Abdul Alim, and Laurent Mathy. "FlowOS." In the 2013 workshop. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2535828.2535836.
Full textSarmad, Muhammad, Juan Luis Gonzalez, and Hyunjoo Jenny Lee. "Training Flownet on Monkaa Dataset." In 2018 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia). IEEE, 2018. http://dx.doi.org/10.1109/icce-asia.2018.8552113.
Full textBotha, Barend W., and Pieter G. Rousseau. "Simulation Investigation of Control Options for Full Load Rejection in the PBMR Closed Cycle Gas Turbine Power Plant." In ASME Turbo Expo 2002: Power for Land, Sea, and Air. ASMEDC, 2002. http://dx.doi.org/10.1115/gt2002-30145.
Full textDosovitskiy, Alexey, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, and Thomas Brox. "FlowNet: Learning Optical Flow with Convolutional Networks." In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.316.
Full textBart´k, Roman, Milan Jaska, Ladislav Nov´k, Vladimir Rovensky, Tomas Skalicky, Martin Cully, Con Sheahan, and Dang Thanh-Tung. "Workflow Optimization with FlowOpt: On Modelling, Optimizing, Visualizing, and Analysing Production Workflows." In 2011 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2011. http://dx.doi.org/10.1109/taai.2011.36.
Full textZhang, Xi, Di Ma, Xu Ouyang, Shanshan Jiang, Lin Gan, and Gady Agam. "Layered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/163.
Full textJun Ma, James Walker, Chaoli Wang, Scott Kuhl, and Ching Kuang Shene. "FlowTour: An Automatic Guide for Exploring Internal Flow Features." In 2014 IEEE Pacific Visualization Symposium (PacificVis). IEEE, 2014. http://dx.doi.org/10.1109/pacificvis.2014.14.
Full textIlg, Eddy, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox. "FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.179.
Full textReports on the topic "Flowout"
C.R. Wilson and T.A. Grant. Data Qulaification Report Flowong Interval Data for Use On the Yucca Mountain Project. Office of Scientific and Technical Information (OSTI), August 2000. http://dx.doi.org/10.2172/893391.
Full textJaffe, Adam, and Manuel Trajtenberg. Flows of Knowledge from Universities and Federal Labs: Modeling the Flowof Patent Citations Over Time and Across Institutional and Geographic Boundari. Cambridge, MA: National Bureau of Economic Research, August 1996. http://dx.doi.org/10.3386/w5712.
Full text