Literatura académica sobre el tema "Uncertainty treatment"
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Artículos de revistas sobre el tema "Uncertainty treatment"
McNAMARA, DAMIAN. "Physicians Face Osteoporosis-Treatment Uncertainty". Caring for the Ages 12, n.º 7 (julio de 2011): 11. http://dx.doi.org/10.1016/s1526-4114(11)60187-x.
Texto completoCheng, C. W., Y. Zheng, B. W. Wessels, J. A. Dorth y D. B. Mansur. "Treatment Planning and Uncertainty Analysis". International Journal of Radiation Oncology*Biology*Physics 93, n.º 3 (noviembre de 2015): E624—E625. http://dx.doi.org/10.1016/j.ijrobp.2015.07.2140.
Texto completoZhang, Siliang, Ping Zhu, Wei Chen y Paul Arendt. "Concurrent treatment of parametric uncertainty and metamodeling uncertainty in robust design". Structural and Multidisciplinary Optimization 47, n.º 1 (24 de mayo de 2012): 63–76. http://dx.doi.org/10.1007/s00158-012-0805-5.
Texto completoCassidy, Rachel y Charles F. Manski. "Tuberculosis diagnosis and treatment under uncertainty". Proceedings of the National Academy of Sciences 116, n.º 46 (29 de octubre de 2019): 22990–97. http://dx.doi.org/10.1073/pnas.1912091116.
Texto completoNorwood, Margaret. "Uncertainty of Treatment for Myalgic Encephalopathy". Physiotherapy 87, n.º 12 (diciembre de 2001): 677–78. http://dx.doi.org/10.1016/s0031-9406(05)61121-2.
Texto completoWeise, K. y H. Zhang. "Uncertainty treatment in Monte Carlo simulation". Journal of Physics A: Mathematical and General 30, n.º 17 (7 de septiembre de 1997): 5971–80. http://dx.doi.org/10.1088/0305-4470/30/17/008.
Texto completoBrown, James D. "Prospects for the open treatment of uncertainty in environmental research". Progress in Physical Geography: Earth and Environment 34, n.º 1 (22 de enero de 2010): 75–100. http://dx.doi.org/10.1177/0309133309357000.
Texto completoBender, Bernice K. y David M. Perkins. "Treatment of Parameter Uncertainty and Variability for a Single Seismic Hazard Map". Earthquake Spectra 9, n.º 2 (mayo de 1993): 165–95. http://dx.doi.org/10.1193/1.1585711.
Texto completoTötsch, Niklas y Daniel Hoffmann. "Classifier uncertainty: evidence, potential impact, and probabilistic treatment". PeerJ Computer Science 7 (4 de marzo de 2021): e398. http://dx.doi.org/10.7717/peerj-cs.398.
Texto completoBelia, E., Y. Amerlinck, L. Benedetti, B. Johnson, G. Sin, P. A. Vanrolleghem, K. V. Gernaey et al. "Wastewater treatment modelling: dealing with uncertainties". Water Science and Technology 60, n.º 8 (1 de octubre de 2009): 1929–41. http://dx.doi.org/10.2166/wst.2009.225.
Texto completoTesis sobre el tema "Uncertainty treatment"
McGowan, Stacey Elizabeth. "Incorporating range uncertainty into proton therapy treatment planning". Thesis, University of Cambridge, 2015. https://www.repository.cam.ac.uk/handle/1810/248787.
Texto completoDurbach, Ian N. "The treatment of uncertainty in multicriteria decision making". Master's thesis, University of Cape Town, 2003. http://hdl.handle.net/11427/15424.
Texto completoThe nature of human decision making dictates that a decision must often be considered under conditions of uncertainty. Decisions may be influenced by uncertain future events, doubts regarding the precision of inputs, doubts as to what the decision maker considers important, and many other forms of uncertainty. The multicriteria decision models that are designed to facilitate and aid decision making must therefore consider these uncertainties if they are to be effective. In this thesis, we consider the treatment of uncertainty in multicriteria decision making (MCDM), with a specific view to investigating the types of uncertainty that are most relevant to MCDM, [and] how the uncertainties identified as relevant may be treated by various different MCDM methodologies.
Strez, Henryk Andrzej Leon. "The treatment of uncertainty in construction price modelling". Bachelor's thesis, University of Cape Town, 1991. http://hdl.handle.net/11427/27115.
Texto completoTowler, Erin L. "Characterizing and incorporating uncertainty in water quality and treatment". Diss., Connect to online resource, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1439443.
Texto completoVrugt, Jasper Alexander. "Towards improved treatment of parameter uncertainty in hydrologic modeling". [S.l. : Amsterdam : s.n.] ; Universiteit van Amsterdam [Host], 2004. http://dare.uva.nl/document/77207.
Texto completoLiu, Xiaohui. "Probability-related treatment of uncertainty in knowledge-based systems". Thesis, Heriot-Watt University, 1988. http://hdl.handle.net/10399/1002.
Texto completoGecchele, Gregorio. "Transportation data analysis. Advances in data mining and uncertainty treatment". Doctoral thesis, Università degli studi di Trieste, 2012. http://hdl.handle.net/10077/7448.
Texto completoNello studio dei sistemi di trasporto l’acquisizione e l’utilizzo di informazioni corrette e aggiornate sullo stato dei sistemi rappresentano da sempre elementi di centrale importanza per la produzione di analisi adeguate ed affidabili. Sfortunatamente in molti ambiti applicativi le informazioni disponibili per le analisi sono invece spesso carenti o di bassa qualità, e il loro utilizzo si traduce in risultati affetti da elevata incertezza e talvolta di dubbia validità. I processi di evoluzione tecnologica che interessano campi quali l’informatica, l’elettronica e le telecomunicazioni stanno rendendo progressivamente più semplice e conveniente l’acquisizione di rilevanti quantità di dati di interesse per le analisi trasportistiche, sia tradizionalmente raccolti per studi trasportistici (ad esempio dati di traffico rilevati su tronchi stradali) sia non direttamente connessi ad un uso trasportistico (ad esempio segnali Bluetooth e GPS provenienti da dispositivi di telefonia mobile). Tuttavia in molti casi l’ampia disponibilità di dati, soprattutto nel secondo caso, non si traduce in immediata spendibilità applicativa. I dati sono infatti spesso disomogenei dal punto di vista informativo, caratterizzati da una qualità non necessariamente elevata e spesso richiedono onerosi processi di verifica e validazione. In questi particolari contesti l’applicazione di tecniche di Data Mining può dimostrarsi una soluzione indubbiamente vantaggiosa. Esse infatti, per loro intrinseca natura, rendono possibile la gestione efficace di grandi quantità di dati e la produzione di risultati sempre più robusti all’aumentare delle dimensioni della base di dati disponibile. Sulla base di queste considerazioni questo lavoro di tesi si è concentrato in primo luogo su un’attenta revisione delle più consolidate tecniche di Data Mining, individuando gli ambiti applicativi, nel campo dei trasporti, in cui esse possono rappresentare dei validi strumenti di analisi. Con il termine Data Mining si fa riferimento al processo di estrazione dell’informazione presente in un certo insieme di dati, finalizzato ad individuare relazioni “nascoste” nei dati stessi o comunque a sintetizzare in modalità nuove la visione su di essi. Esso rappresenta una parte di un più ampio processo di estrazione della conoscenza, che inizia con un’accurata selezione e trasformazione dei dati disponibili (come detto i dati sottoposti a “mining” sono spesso raccolti con altri obiettivi) e si conclude con un’attenta interpretazione e valutazione dei risultati. Uno schema di classificazione generalmente accettato suddivide le tecniche di Data Mining in sei categorie in rapporto alla funzione considerata: stima (reti neurali, modelli di regressione, alberi decisionali), previsione (reti neurali, alberi decisionali), classificazione (k-nearest neighbour, alberi decisionali, reti neurali), raggruppamento (tecniche di clustering, Self-Organising-Maps), associazione (regole di associazione) e descrizione (regole di associazione, clustering, alberi decisionali). Nel presentare un quadro d’insieme dell’ampia letteratura esistente in materia, uno specifico rilievo è stato dato alle più consolidate tecniche di classificazione, raggruppamento e associazione, in quanto maggiormente impiegate nei diversi contesti applicativi. Successivamente è stato tracciato uno stato dell’arte per ciò che attiene le applicazioni in ambito trasportistico. In tal senso la revisione dei lavori prodotti ha evidenziato la notevole flessibilità d’uso di queste tecniche e la loro crescente diffusione applicativa. Molti sono infatti i filoni di ricerca che hanno beneficiato di queste tecniche innovative; tra questi nel lavoro di tesi si sono evidenziati alcuni tra i più interessanti: la previsione a breve termine dei flussi di traffico da dati storici o in real-time (traffic forecasting), l’identificazione e la quantificazione dei fattori che influenzano i fenomeni di incidentalità, l’analisi di sistemi di gestione delle pavimentazioni stradali e di sistemi di monitoraggio del traffico. La seconda parte della tesi si è invece focalizzata su un’applicazione delle tecniche di Data Mining allo studio del funzionamento di un sistema viario, attraverso una revisione critica della Procedura FHWA (Federal Highway Administration) per il monitoraggio del traffico stradale. La scelta di questo filone di ricerca è data dal fatto che la raccolta di informazioni sui volumi di traffico è un aspetto rilevante nell’attività di pianificazione dei trasporti (ambito stradale), quale componente significativa del processo conoscitivo. D’altra parte i costi legati alla gestione dei sistemi di monitoraggio, sia per attrezzature che per personale, richiedono una crescente attenzione alla loro progettazione, al fine di ottenere la massima qualità dei risultati. Negli Stati Uniti la FHWA definisce periodicamente alcune linee guida per migliorare questi aspetti attraverso la Traffic Monitoring Guide (2001) e ha raggiunto progressivamente un ruolo di riferimento per altre agenzie dello stesso tipo in altre parti del mondo, Italia compresa. Tale procedura è basata sull’uso congiunto di rilievi di diversa durata (rilievi in continuo con strumenti fissi e rilievi di breve durata con apparecchiature portatili) ed è finalizzata principalmente alla stima del Traffico Giornaliero Medio Annuo (Annual Average Daily Traffic, AADT). L’analisi della letteratura esistente ha individuato la lacuna principale della procedura FHWA nella determinazione dei gruppi tipologici di strade sulla base dei profili temporali di traffico e nell’assegnazione delle sezioni monitorate con rilievi di breve durata a questi gruppi. L’approccio elaborato si è pertanto posto l’obiettivo di migliorare la procedura relativamente a questi due aspetti rilevanti. Per trattare l’esistenza di situazioni di incerta attribuzione di una sezione stradale ad un certo gruppo tipologico, specie quando non è semplice fornire una chiara definizione in termini trasportistici (ad esempio strada “pendolare” o “turistica”), sono state adottate tecniche di Fuzzy Clustering, garantendo un’opportuna trattazione formale del problema. Per quanto concerne il secondo aspetto, le sezioni non monitorate in continuo vengono inserite nel gruppo tipologico più simile rispetto ai profili temporali di traffico osservati. Per effettuare l’assegnazione di queste sezioni ai gruppi tipologici, l’approccio proposto ha utilizzato una Rete Neurale Artificiale, opportunamente progettata per mantenere l’incertezza presente nella fase di creazione dei gruppi fino alla fine del processo. L’output della rete è infatti rappresentato dall’insieme delle probabilità di appartenenza del rilievo di breve durata ai diversi gruppi tipologici ed è interpretato utilizzando la teoria di Dempster-Shafer. Le misure di incertezza associate all’output (indici di non-specificità e discordanza) permettono di descrivere sinteticamente la qualità dell’informazione disponibile. L’approccio proposto è stato implementato considerando i dati di monitoraggio provenienti dal programma SITRA (Sistema Informativo TRAsporti) della Provincia di Venezia. Rispetto all’ambito applicativo di interesse è stata verificata la validità dell’approccio, confrontando i risultati ottenuti nella stima dell’AADT con precedenti approcci proposti in letteratura. L’analisi comparativa dei risultati ha permesso di rilevare una migliore accuratezza delle stime e soprattutto la possibilità, assente nei precedenti approcci, di evidenziare eventuali carenze informative (dovute all’esiguo numero di dati) e la necessità di procedere con ulteriori rilievi di traffico. I risultati positivi ottenuti in questa fase sperimentale hanno permesso di avviare il progetto per la realizzazione di uno strumento software di immediata spendibilità applicativa
In the study of transportation systems, the collection and the use of correct information of the state of the system represent a central point for the development of reliable and proper analyses. Unfortunately in many application fields information is generally obtained using limited, scarce and low-quality data and their use produces results affected by high uncertainty and in some cases low validity. Technological evolution processes which interest different fields, including Information Technology, electronics and telecommunications make easier and less expensive the collection of large amount of data which can be used in transportation analyses. These data include traditional information gathered in transportation studies (e.g. traffic volumes in a given road section) and new kind of data, not directly connected to transportation needs (i.e. Bluetooth and GPS data from mobile phones). However in many cases, in particular for the latter case, this large amount of data cannot be directly applied to transportation problems. Generally there are low-quality, non-homogeneous data, which need time consuming verification and validation process to be used. Data Mining techniques can represent an effective solution to treat data in these particular contexts since they are designed to manage large amount of data producing results whose quality increases as the amount of data increases. Based on these facts, this thesis first presents a review of the most well-established Data Mining techniques, identifying application contexts in transportation field for which they can represent useful analysis tools. Data mining can be defined as the process of exploration and analysis which aims to discover meaningful patterns and ‘’hidden’’ rules in the set of data under analysis. Data Mining could be considered a step of a more general Knowledge Discovery in Databases Process, which begins with selection, pre-processing and transformation of data (“mined” data are generally collected for reasons different from the analysis) and is completed with the interpretation and evaluation of results. A classification scheme generally accepted identifies six categories of DM techniques, which are related to the objective one would achieve from the analysis: estimation (neural networks, regression models, decision trees), prediction (neural networks, decision trees), classification (k-nearest neighbor, decision trees, neural networks), clustering (clustering techniques, Self-Organizing-Maps), affinity grouping or association (association rules) and profiling (association rules). In the review of the wide literature concerning Data Mining methods, particular attention has been devoted to the well-established technique of clustering, classification and association, since they are the most applied in different application contexts. The literature review process has been further extended to Data Mining applications in the transportation field. This review highlights the great flexibility of use of these techniques and the increasing number of applications. Many research topics have taken advantages of these innovative tools and some of them are presented due to their interest: short-term traffic flow forecasting from historical and real-time data, identification and quantification of factor risks in accident analysis, analysis of pavement management systems and traffic monitoring systems. The second part of the thesis has focused on the application of Data Mining techniques to road system analysis, through a critical review of U.S. Federal Highway Administration (FHWA) traffic monitoring approach. The choice of this topic is due to the fact that traffic monitoring activities represent a relevant aspect of highway planning activities, as a part of the knowledge process. However data collection activities produce relevant management costs, both for equipment and personnel, therefore monitoring programs need to be designed with attention to obtain the maximum quality of results. In the U.S.A., the Federal Highway Administration (FHWA) provides guidance for improving these aspects by way of its Traffic Monitoring Guide (TMG) (FHWA, 2001), which has a reference role for other similar agencies in the world. The FHWA procedure is based on two types of counts (short duration counts taken with portable traffic counters and continuous counts taken with fixed counters) and has the main objective of determine the Annual Average Daily Traffic (AADT). Critical review of literature on this topic has pointed out that the most critical aspects of this procedure are the definition of road groups based on traffic flow patterns and the assignment of a section to a road group using short counts. The proposed approach has been designed to solve both issues. The first issue is related to situations for which road section could belong to more than one road group, and the groups cannot be easily defined in transportation terms, (e.g. “commuter road”, “recreational road”). The proposed approach introduces Fuzzy Clustering techniques, which adopt an analytical framework consistent with this kind of uncertainty. Concerning the second issue, road sections monitored with short counts are assigned to the road group with more similar traffic patterns. In the proposed approach an Artificial Neural Network is implemented to assign short counts to roads groups. The Neural network is specifically designed to maintain the uncertainty related to the definition of road groups until the end of the estimation process. In fact the output of the Neural Network are the probabilities that the a specific short counts belongs to the road groups. These probabilities are interpreted using the Dempster-Shafer theory; measures of uncertainty related to the output (indices of non-specificity and discord) provide an assessment of the quality of information in a synthetic manner. The proposed approach have been implement on a case study, using traffic data from SITRA (Sistema Informativo TRAsporti) monitoring program of the Province of Venice. In this specific context the approach has been validated and the results obtained (AADT estimates) from the proposed method have been compared with those obtained by two approaches proposed in previous studies. The comparative analysis highlights that the proposed approach increases the accuracy of estimates and gives indication of the quality of assignment (depending on sample size) and suggests the need for additional data collection. The positive results obtained in the experimental phase of the research have led to the design of a software tool to be used in next future in real world applications.
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Tyler, David Keith. "Improved estimation of uncertainty in flow measurement at sewage treatment works". Thesis, University of Hertfordshire, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409476.
Texto completoGrabaskas, David. "Efficient Approaches to the Treatment of Uncertainty in Satisfying Regulatory Limits". The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1345464067.
Texto completoNiculescu, Mihai. "Towards a Unified Treatment of Risk and Uncertainty in Choice Research". University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1249493228.
Texto completoLibros sobre el tema "Uncertainty treatment"
Great Britain. Department of the Environment, Transport and the Regions., ed. Treatment of uncertainty in the national road traffic forecasts. London: Department of the Environment, Transport and the Regions, 1998.
Buscar texto completoGil-Aluja, Jaime, Antonio Terceño-Gómez, Joan Carles Ferrer-Comalat, José M. Merigó-Lindahl y Salvador Linares-Mustarós, eds. Scientific Methods for the Treatment of Uncertainty in Social Sciences. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19704-3.
Texto completoJoão Inácio da Silva Filho. Uncertainty treatment using paraconsistent logic: Introducing paraconsistent artificial neural networks. Amsterdam: IOS Press, 2010.
Buscar texto completoFreedom from obsessive-compulsive disorder: A personalized recovery program for living with uncertainty. New York: Berkley Books, 2004.
Buscar texto completoAven, Terje. Uncertainty in risk assessment: The representation and treatment of uncertainties by probabilistic and non-probabilistic methods. Chichester, West Sussex, United Kingdom: Wiley, 2014.
Buscar texto completoFinancial derivatives: Disparate tax treatment and information gaps create uncertainty and potential abuse : report to congressional requesters. Washington, D.C.]: U.S. Govt. Accountability Office, 2011.
Buscar texto completoCarneiro, Pedro. Estimating distributions of treatment effects with an application to the returns to schooling and measurement of the effects of uncertainty on college choice. Cambridge, Mass: National Bureau of Economic Research, 2003.
Buscar texto completoNational Hospice Organization (U.S.). Ethics Committee. Decisions in hospice: Guidelines for making decisions about the place or mode of treatment when there is conflict or uncertainty among the patient, primary care-giver, family members, primary physician, and hospice staff. Arlington, VA: National Hospice Organization, 1985.
Buscar texto completoBeyond second opinions: Making choices about fertility treatment. Berkeley: University of California Press, 1998.
Buscar texto completoUnited States. General Accounting Office., ed. Hazardous waste: Future availability of and need for treatment capacity are uncertain : report to congressional requesters. Washington, D.C: GAO, 1988.
Buscar texto completoCapítulos de libros sobre el tema "Uncertainty treatment"
Scaglia, Gustavo, Mario Emanuel Serrano y Pedro Albertos. "Uncertainty Treatment". En Linear Algebra Based Controllers, 103–16. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42818-1_7.
Texto completoBi, Sifeng y Michael Beer. "Overview of Stochastic Model Updating in Aerospace Application Under Uncertainty Treatment". En Uncertainty in Engineering, 115–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83640-5_8.
Texto completoJacoby, Ryan J. "Intolerance of uncertainty." En Clinical handbook of fear and anxiety: Maintenance processes and treatment mechanisms., 45–63. Washington: American Psychological Association, 2020. http://dx.doi.org/10.1037/0000150-003.
Texto completoMenzies, Tim, Eliza Chiang, Martin Feather, Ying Hu y James D. Kiper. "Condensing Uncertainty via Incremental Treatment Learning". En Software Engineering with Computational Intelligence, 319–61. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0429-0_12.
Texto completoGil-Aluja, Jaime. "Economic Treatment of Fixed Assets". En Fuzzy Sets in the Management of Uncertainty, 177–216. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39699-4_6.
Texto completoGonzález, E., A. Suárez, C. Moreno y F. Artigue. "Uncertainty treatment in a surface filling mobile robot". En Reasoning with Uncertainty in Robotics, 294–306. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0013968.
Texto completoDi Nola, Antonio. "MV Algebras in the Treatment of Uncertainty". En Fuzzy Logic, 123–31. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_12.
Texto completoCampos, Luis M., Jörg Gebhardt y Rudolf Kruse. "Axiomatic treatment of possibilistic independence". En Symbolic and Quantitative Approaches to Reasoning and Uncertainty, 77–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60112-0_10.
Texto completoMelchers, R. E. "On the treatment of uncertainty information in PRA". En Probabilistic Risk and Hazard Assessment, 13–26. London: Routledge, 2022. http://dx.doi.org/10.1201/9780203742037-2.
Texto completoVal, Anabel del, Olivier Chazot y Thierry Magin. "Uncertainty Treatment Applications: High-Enthalpy Flow Ground Testing". En Optimization Under Uncertainty with Applications to Aerospace Engineering, 507–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60166-9_16.
Texto completoActas de conferencias sobre el tema "Uncertainty treatment"
Parker, Eric J., Chiara Traverso y Marco Pedemonte. "Treatment of Uncertainty in Liquefaction Analysis". En GeoCongress 2006. Reston, VA: American Society of Civil Engineers, 2006. http://dx.doi.org/10.1061/40803(187)137.
Texto completoYasumuro, Yoshihiro, Koichi Hosomi, Yoichi Saitoh y Taiga Matsuzaki. "Uncertainty assessment of target localization for rTMS treatment". En 2012 ICME International Conference on Complex Medical Engineering (CME). IEEE, 2012. http://dx.doi.org/10.1109/iccme.2012.6275590.
Texto completoMegfas, D., J. Serrano y C. de Prada. "Uncertainty treatment in GPC: Design of T polynomial". En 1997 European Control Conference (ECC). IEEE, 1997. http://dx.doi.org/10.23919/ecc.1997.7082110.
Texto completoTaylor, Craig, William Graf, Yajie (Jerry) Lee, Charles Huyck y Zhenghui Hu. "Sample Treatment of Uncertainties in Earthquake Portfolio Risk Analysis". En First International Symposium on Uncertainty Modeling and Analysis and Management (ICVRAM 2011); and Fifth International Symposium on Uncertainty Modeling and Anaylsis (ISUMA). Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41170(400)30.
Texto completoHuang, Chin-Jung y Ying-Hong Lin. "A Conflict Treatment Model for Uncertainty Rule-based Knowledge". En Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.10.
Texto completoBenderskaya, Elena N. "Chaotification as a Method for the Treatment of Uncertainty". En 2021 International Conference on Data Science and Its Applications (ICoDSA). IEEE, 2021. http://dx.doi.org/10.1109/icodsa53588.2021.9617490.
Texto completoBermeo Varon, Leonardo Antonio, Helcio Rangel Barreto Orlande y Guillermo Enrique Eliçabe. "State Estimation Problem in a Complex Domain: RF Hyperthermia Treatment using Nanoparticles". En 3rd International Symposium on Uncertainty Quantification and Stochastic Modeling. Rio de Janeiro, Brazil: ABCM Brazilian Society of Mechanical Sciences and Engineering, 2015. http://dx.doi.org/10.20906/cps/usm-2016-0058.
Texto completoYassine, Abdul-Amir, Lothar D. Lilge y Vaughn Betz. "Tolerating uncertainty: photodynamic therapy planning with optical property variation". En Optical Methods for Tumor Treatment and Detection: Mechanisms and Techniques in Photodynamic Therapy XXVIII, editado por David H. Kessel y Tayyaba Hasan. SPIE, 2019. http://dx.doi.org/10.1117/12.2508580.
Texto completoMeyer, P. D. y S. J. Cohen. "Treatment of Uncertainty in Groundwater Modeling: A (Limited) Research Perspective". En World Environmental and Water Resources Congress 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41114(371)80.
Texto completoMcFarland, John, Barron J. Bichon y David S. Riha. "A Probabilistic Treatment of Multiple Uncertainty Types: NASA UQ Challenge". En 16th AIAA Non-Deterministic Approaches Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2014. http://dx.doi.org/10.2514/6.2014-1500.
Texto completoInformes sobre el tema "Uncertainty treatment"
Yang, David Y. Incorporating Model Parameter Uncertainty into Prostate IMRT Treatment Planning. Fort Belvoir, VA: Defense Technical Information Center, abril de 2005. http://dx.doi.org/10.21236/ada439169.
Texto completoPawlicki, Todd A. Integrating Organ Motion and Setup Uncertainty into Optimization of Modulated Electron Beam Treatment of Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2001. http://dx.doi.org/10.21236/ada406052.
Texto completoPawlicki, Todd A. Integrating Organ Motion and Setup Uncertainty into Optimization of Modulated Electron Beam Treatment of Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2003. http://dx.doi.org/10.21236/ada420164.
Texto completoMarold, Juliane, Ruth Wagner, Markus Schöbel y Dietrich Manzey. Decision-making in groups under uncertainty. Fondation pour une culture de sécurité industrielle, febrero de 2012. http://dx.doi.org/10.57071/361udm.
Texto completoSutton, M., J. Blink, H. Greenberg y M. Sharma. ADVANCED NUCLEAR FUEL CYCLE EFFECTS ON THE TREATMENT OF UNCERTAINTY IN THE LONG-TERM ASSESSMENT OF GEOLOGIC DISPOSAL SYSTEMS - EBS INPUT. Office of Scientific and Technical Information (OSTI), abril de 2012. http://dx.doi.org/10.2172/1044938.
Texto completoCarneiro, Pedro, Karsten Hansen y James Heckman. Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College. Cambridge, MA: National Bureau of Economic Research, marzo de 2003. http://dx.doi.org/10.3386/w9546.
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