Relevant bibliographies by topics / Dynamic monitoring

Academic literature on the topic 'Dynamic monitoring'

Author: Grafiati
Published: 10 December 2022
Last updated: 11 March 2023

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Journal articles on the topic "Dynamic monitoring"

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Drummond, John. "Dynamic Task Monitoring." Electronic Notes in Theoretical Computer Science 25 (1999): 35–37. http://dx.doi.org/10.1016/s1571-0661(04)00129-x.

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Ge, Yongquan, Chengxin Yu, Xiaodong Liu, and Ronghui Wang. "Monitoring dynamic deformation of communication tower using photography dynamic monitoring system." IOP Conference Series: Earth and Environmental Science 558 (September 5, 2020): 022061. http://dx.doi.org/10.1088/1755-1315/558/2/022061.

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Natalia N., Popova. "Factor Dynamic Model of Pedagogical Monitoring." Scholarly Notes of Transbaikal State University 16, no. 1 (March 2021): 13–19. http://dx.doi.org/10.21209/2658-7114-2021-16-1-13-19.

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The article examines the topical problems of scientific validity and practice-oriented monitoring in education in the context of increasing requirements for individualization, diversification and differentiation of education. Based on the data of psychological and pedagogical diagnostics, a dynamic factor model of pedagogical monitoring has been developed. For this, the essential characteristics of pedagogical monitoring as a continuous, dynamic and multifactorial technology for supporting students have been determined. Representative representation of the monitoring results was carried out on a sample of students from a number of educational institutions of the Transbaikal Territory, numbering 250 people. The focus of the article is on the creativity of students as a manifestation of their non-standard and individuality, capable of ensuring their success in professional activities. The results of dynamic monitoring of students creativity indicators collected using a battery of tests of verbal and non-verbal creativity, as well as a number of personal techniques are presented. It has been empirically proven that the factorial dynamic model, built on measuring the level of development of the main indicators of creativity, ensures the comparability of results and the identification of latent factors (activity, personality creativity and values). These explain the presence or absence of dynamics in the studied indicators (creative attitude to the profession, originality, uniqueness and personality traits). The data obtained demonstrate the presence of statistically significant changes at the level of originality, the uniqueness of the developed fluency and flexibility in the process of the correlation conditioning of the dynamics of the integral characteristics of the personality (values: traditions, hedonism; self-esteem; emotionality, sociability, high normality of behavior and sensitivity). Keywords: pedagogical monitoring, creativity, dynamic model, factor analysis, students
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Qiu, Peihua, Xuemin Zi, and Changliang Zou. "Nonparametric Dynamic Curve Monitoring." Technometrics 60, no. 3 (May 7, 2018): 386–97. http://dx.doi.org/10.1080/00401706.2017.1361340.

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Fareed, Abid, and Abita Devi. "IOT a Dynamic Approach for Smart System Monitoring on Soil." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 2373–76. http://dx.doi.org/10.31142/ijtsrd18359.

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Ullah, Najeeb, Faizullah Khan, Abdul Ali Khan, Surat Khan, Abdul Wahid Tareen, Muhammad Saeed, and Akbar Khan. "Optimal Real-time Static and Dynamic Air Quality Monitoring System." Indian Journal of Science and Technology 13, no. 1 (January 20, 2020): 1–12. http://dx.doi.org/10.17485/ijst/2020/v13i01/148375.

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Solan, Eilon, and Chang Zhao. "Dynamic monitoring under resource constraints." Games and Economic Behavior 129 (September 2021): 476–91. http://dx.doi.org/10.1016/j.geb.2021.06.009.

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Melcer, Jozef, Daniela Kuchárová, and Gabriela Lajčáková. "The bridge dynamic properties monitoring." MATEC Web of Conferences 265 (2019): 03009. http://dx.doi.org/10.1051/matecconf/201926503009.

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The SNP Bridge over the Danube in Bratislava represents an attractive steel cable-stayed bridge. Its length is 431.8 m. The submitted paper describes the methodology of experimental testing and presents some results of the loading test. During this test it was observed that the dilatation unit on the right water side shows some failure. Due to this failure the dilatation unit acts as a generator of vibration of the end bridge span. On the basis of experimental measurements this failure was detected and the dilatation unit was renovated.
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Barbos, Andrei. "Dynamic contracts with random monitoring." Journal of Mathematical Economics 85 (December 2019): 1–16. http://dx.doi.org/10.1016/j.jmateco.2019.07.008.

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Ateniese, G., C. Riley, and C. Scheideler. "Survivable Monitoring in Dynamic Networks." IEEE Transactions on Mobile Computing 5, no. 9 (September 2006): 1242–54. http://dx.doi.org/10.1109/tmc.2006.138.

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Dissertations / Theses on the topic "Dynamic monitoring"

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Miallaret, Sophie. "Dynamic Monitoring Measures." Thesis, Université Clermont Auvergne‎ (2017-2020), 2019. http://www.theses.fr/2019CLFAC091.

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Les mesures sont des actes quotidiens, elles nous donnent beaucoup d'informations et permettent de prendre des décisions. L'analyse des mesures peut nous permettre d'en apprendre plus sur notre environnement, mais l'erreur d'une mesure peut avoir des conséquences importantes dans certains domaines. Dans une première partie, nous proposons, grâce à l'étude de mesures d'analyses sanguines réalisées au CHU de Clermont-Ferrand, une procédure permettant de détecter les dérives des analyseurs de laboratoires de biologie médicale, se basant sur les mesures d'analyses de patients. Après une analyse descriptive des données, la méthode mise en place, utilisant des méthodes de détection de ruptures de séries temporelles, est testée pour des simulations de ruptures représentant des décalages, des imprécisions ou des dérives d'analyseurs pour différents paramètres biologiques mesurés. La méthode est adaptée pour deux scénarios : lorsque l'on connaît ou non le service hospitalier des patients. L'étude est complétée par une analyse de l'impact de l'incertitude de mesure sur les analyses des patients. Dans une seconde partie nous étudions des mesures de formes de cendres volcaniques réalisées au Laboratoire Magmas et Volcans de l'Université Clermont Auvergne, dans le but de déterminer un lien entre les lieux de collecte et les formes des particules. Après avoir montré la dépendance entre ces paramètres, nous proposons, grâce une méthode de classification, un regroupement des particules représentant différentes populations dépendantes de la distance entre les lieux de collecte et le cratère du volcan
The measures are daily actions, they give us a lot of information and allow us to make decisions. The analysis of measures can allow us to learn more about our environment, but the error of a measure can have important consequences in certain areas. In a first part, we propose, thanks to the study of blood test measurements carried out at the CHU of Clermont-Ferrand, a procedure for detecting deviations from medical biology laboratory analyzers based on patient analysis measurements. After a descriptive analysis of the data, the method put in place, using methods of detection of breaks of time series, is tested for simulations of breaks representing offsets, imprecision or drifts of machine for different measured biological parameters. The method is adapted for two scenarios: when the patient's hospital service is known or not. The study is supplemented by an analysis of the impact of measurement uncertainty on patient analyses. In a second part we study measurements of volcanic ash forms made at “Laboratoire Magmas et Volcans” of the Clermont Auvergne University, in order to determine a link between the collection locations and the forms of the particles. After showing the dependence between these parameters, we propose, using a classification method, a grouping of particles representing different populations depending on the distance between the collection locations and the volcano crater
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Gueron, Y. "Monitoring and heterogeneity in dynamic games." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1402474/.

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In this thesis we study the impact of monitoring and heterogeneity on the set of equilibria of dynamic games. In Chapter 1 we show how heterogeneity in time preferences can help create new intertemporal incentives. Proving the folk theorem in a game with three or more players usually requires imposing restrictions on the dimensionality of the stage-game payoffs. Considering a class of games in which those restrictions do not hold, we show how to recover a folk theorem by allowing time preferences to vary across players. In Chapters 2 and 3 we show how a small degree of imperfection in the monitoring technology can have large effects on the set of equilibria of dynamic games. We study a dynamic voluntary contribution game with irreversibility and a game with an asymptotically finite horizon. In both settings, when monitoring is perfect, players can cooperate and obtain payoffs in the repeated game that are strictly greater than the payoffs from the unique inefficient stage-game equilibrium. We show however that introducing an arbitrarily small amount of noise in the monitoring technology can cause a complete breakdown in cooperation. Finally in Chapter 4 we investigate how information is transmitted in a revision game with one-sided incomplete information. Players aim to coordinate on an action which depends on an unknown state of the world and players can only revise their actions stochastically during a preparation stage, at the end of which the prepared action profile is implemented. Miscoordination arises from the possibility of no longer receiving revision opportunities until the deadline. We show that close to the deadline no information is transmitted and that far from the deadline the uninformed player prefers to be miscoordinated.
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Burns, James P. A. "Dynamic modeling and monitoring of bridge decks." Thesis, University of Glasgow, 1986. http://theses.gla.ac.uk/1207/.

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Iketubosin, P. P. "Studies on non-linear dynamic process monitoring." Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/7455.

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Owing to the numerous benefits of process monitoring, the subject has attracted a lot of attention in the last two decades. Process monitoring is an art of identifying abnormal deviations in a process from the normal operating condition using various techniques. Generally, the development of these monitoring techniques is geared towards applying these techniques to industrial processes. In addition, most industrial processes are dynamic and non-linear in nature. Therefore, in the development of the monitoring algorithms, the dynamic as well as the non-linear properties of the plant should be taken into consideration. Process monitoring techniques like the Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression analysis were developed based on the assumption that the process data is normally distributed. Nevertheless, this assumption of normality is invalid for most industrial processes due to the non-linear nature of these plants. For such processes, the distribution of the process variables in general will be non-Gaussian, therefore making the widely applied PCA and PLS approaches inappropriate for the monitoring of plants. To address this limitation of the PCA and PLS for Dynamic processes, the Dynamic PCA (DPCA) and dynamic PLS (DPLS) approaches were developed. The challenge of efficiently monitoring process plants with dynamic and non-linear characteristics is the motivation for this study. The overall aim of this study is to develop process monitoring strategies that are able to take the dynamic and nonlinear properties of the plant into account. With these strategies, more efficient performance monitoring of the plant can be achieved. Cont/d.
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Best, Nicola Grace. "Dynamic models for post-transplant patient monitoring." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321024.

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Nicholson, Ann Elizabeth. "Monitoring discrete environments using dynamic belief networks." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306008.

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Zeileis, Achim, Friedrich Leisch, Christian Kleiber, and Kurt Hornik. "Monitoring structural change in dynamic econometric models." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2002. http://epub.wu.ac.at/1296/1/document.pdf.

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The classical approach to testing for structural change employs retrospective tests using a historical data set of a given length. Here we consider a wide array of fluctuation-type tests in a monitoring situation - given a history period for which a regression relationship is known to be stable, we test whether incoming data are consistent with the previously established relationship. Procedures based on estimates of the regression coefficients are extended in three directions: we introduce (a) procedures based on OLS residuals, (b) rescaled statistics and (c) alternative asymptotic boundaries. Compared to the existing tests our extensions offer better power against certain alternatives, improved size in finite samples for dynamic models and ease of computation respectively. We apply our methods to two data sets, German M1 money demand and U.S. labor productivity.
Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Caniglia, Ellen C. "Dynamic Monitoring Strategies for HIV-Positive Individuals." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:27201722.

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The benefits of immunologic and virologic monitoring for the management of HIV-positive individuals are well established. However, the optimal frequency with which CD4 cell count and HIV RNA should be monitored remains unknown. In this dissertation, we use observational data from two collaborations of prospective cohort studies from high-income countries to estimate the effect of CD4 cell count and HIV RNA monitoring strategies on clinical, virologic, and immunologic outcomes in virologically suppressed HIV-positive patients. In Chapter 1, we compare three CD4 cell count and HIV-RNA monitoring strategies applied to virologically suppressed individuals on combined antiretroviral therapy (cART) without AIDS: once every (i) 3±1 months, (ii) 6±1 months, and (iii) 9-12 ±1 months. We find that monitoring frequency can be decreased from every 3 months to every 6, 9, or 12 months in the short term with respect to clinical outcomes. In Chapter 2, we compare strategies corresponding to three CD4 cell count thresholds at which monitoring frequency is decreased from every 3-6 months to every 9-12 months: 200 cells/µl, 350 cells/µl, and 500 cells/µl. We find that decreasing monitoring from every 3-6 months to every 9-12 months while CD4 cell count>200 cells/µl does not worsen the short-term clinical and immunologic outcomes of HIV-positive, virologically suppressed individuals on cART without AIDS. Our estimates also suggest that decreasing monitoring frequency when CD4 cell count>200 cells/µl compared with when CD4 cell count>500 cells/µl may result in an increased risk of virologic failure at 24 months of follow-up. In Chapter 3, we compare joint monitoring and treatment switching strategies. The strategies expand on those described in Chapter 2 by including two HIV-RNA threshold at which individuals should switch to a new antiretroviral regimen: 200 copies/ml and 1,000 copies/ml. We find that the studied monitoring-switching strategies have little impact on the short-term clinical outcomes of HIV-positive individuals on cART. In summary, we illustrate an approach to compare monitoring strategies in HIV-positive individuals, and provide estimates of the comparative effectiveness of strategies used in clinical practice. Since effects of different monitoring strategies could take years to materialize, longer follow-up is needed to fully evaluate this question.
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Samuel, Raphael Tari. "Nonlinear dynamic process monitoring using kernel methods." Thesis, Cranfield University, 2016. http://dspace.lib.cranfield.ac.uk/handle/1826/11833.

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The application of kernel methods in process monitoring is well established. How- ever, there is need to extend existing techniques using novel implementation strate- gies in order to improve process monitoring performance. For example, process monitoring using kernel principal component analysis (KPCA) have been reported. Nevertheless, the e ect of combining kernel density estimation (KDE)-based control limits with KPCA for nonlinear process monitoring has not been adequately investi- gated and documented. Therefore, process monitoring using KPCA and KDE-based control limits is carried out in this work. A new KPCA-KDE fault identi cation technique is also proposed. Furthermore, most process systems are complex and data collected from them have more than one characteristic. Therefore, three techniques are developed in this work to capture more than one process behaviour. These include the linear latent variable-CVA (LLV-CVA), kernel CVA using QR decomposition (KCVA-QRD) and kernel latent variable-CVA (KLV-CVA). LLV-CVA captures both linear and dynamic relations in the process variables. On the other hand, KCVA-QRD and KLV-CVA account for both nonlinearity and pro- cess dynamics. The CVA with kernel density estimation (CVA-KDE) technique reported does not address the nonlinear problem directly while the regular kernel CVA approach require regularisation of the constructed kernel data to avoid com- putational instability. However, this compromises process monitoring performance. The results of the work showed that KPCA-KDE is more robust and detected faults higher and earlier than the KPCA technique based on Gaussian assumption of process data. The nonlinear dynamic methods proposed also performed better than the aforementioned existing techniques without employing the ridge-type regularisation.
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Thollot, Raphaël. "Dynamic situation monitoring and Context-Aware BI recommendations." Phd thesis, Ecole Centrale Paris, 2012. http://tel.archives-ouvertes.fr/tel-00718917.

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The amount of information generated and maintained by information systems and their users leads to the increasingly important concern of information overload. Personalized systems have thus emerged to help provide more relevant information and services to the user. In particular, recommender systems appeared in the mid 1990's and have since then generated a growing interest in both industry and academia. Besides, context-aware systems have been developed to model, capture and interpret information about the user's situation, generally in dynamic and heterogeneous environments. Decision support systems like Business Intelligence (BI) platforms also face usability challenges as the amount of information available to knowledge workers grows. Remarkably, we observe that only a small part of personalization and recommendation techniques have been used in the context of data warehouses and analysis tools. Therefore, our work aims at exploring synergies of recommender systems and context-aware systems to develop personalization and recommendation scenarios suited in a BI environment. In response to this, we develop in our work an open and modular situation management platform using a graph-based situation model. Besides, dynamic aspects are crucial to deal with context data which is inherently time-dependent. We thus define two types of active components to enable dynamic maintenance of situation graphs, activation rules and operators. In response to events which can describe users' interactions, activation rules - defined using the event-condition-action framework - are evaluated thanks to queries on underlying graphs, to eventually trigger appropriate operators. These platform and framework allow us to develop and support various recommendation and personalization scenarios. Importantly, we design a re-usable personalized query expansion component, using semantics of multi-dimensional models and usage statistics from repositories of BI documents like reports or dashboards. This component is an important part of another experimentation we realized, Text-To-Query. This system dynamically generates multi-dimensional queries to illustrate a text and support the knowledge worker in the analysis or enrichment of documents she is manipulating. Besides, we also illustrate the integration and usage of our graph repository and situation management frameworks in an open and extensible federated search project, to provide background knowledge management and personalization.
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Books on the topic "Dynamic monitoring"

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Dykes, Alan P., Mark Mulligan, and John Wainwright, eds. Monitoring and Modelling Dynamic Environments. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118649596.

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John, Wainwright, Alan P. Dykes, and Mark Mulligan. Monitoring and modelling dynamic environments. West Sussex, UK: John Wiley & Sons, Ltd, 2016.

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Hasemann, Jorg-Michael. Planning and monitoring in dynamic environments. Espoo, Finland: VTT, Technical Research Centre of Finland, 1995.

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J, Chaouki, Larachi Faïcal, and Duduković Milorad P. 1944-, eds. Non-invasive monitoring of multiphase flows. Amsterdam: Elsevier, 1997.

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Veerapen, Parmaseeven Pillay. Recurrence relationships and model monitoring for dynamic linear models. [s.l.]: typescript, 1991.

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Laitakari, Juhani. Dynamic context monitoring for adaptive and context-aware applications. [Espoo, Finland]: VTT Technical Research Centre of Finland, 2007.

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Armenakis. Displacement monitoring by integrating on-line photogrammetric observations with dynamic information. Fredericton, N.B: Dept. of Surveying Engineering, University of New Brunswick, 1987.

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Blok, Connie A. Dynamic visualization variables in animation to support monitoring of spatial phenomena. Utrecht: Koninklijk Nederlands Aardrijkskundig Genootschap ; International Geographical Union Section The Netherlands, 2005.

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Dynamic visualization variables in animation to support monitoring of spatial phenomena. Utrecht: Koninklijk Nederlands Aardrijkskundig Genootschap, Faculteit Geowetenschappen, Universiteit Utrecht, 2005.

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Ramesh, Kadali, ed. Dynamic modeling, predictive control and performance monitoring: A data-driven subspace approach. London: Springer, 2008.

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Book chapters on the topic "Dynamic monitoring"

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Ellis, B. R. "Dynamic testing." In Monitoring Building Structures, 68–92. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4757-5894-8_4.

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Cornejo, Roger. "Monitoring." In Dynamic Oracle Performance Analytics, 175–89. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4137-0_9.

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Ge, Zhiqiang, and Zhihuan Song. "Dynamic Process Monitoring." In Advances in Industrial Control, 131–46. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4513-4_10.

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Aldrich, Chris, and Lidia Auret. "Dynamic Process Monitoring." In Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods, 281–339. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5185-2_7.

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Messerotti, M. "Solar Activity Monitoring." In The Dynamic Sun, 69–93. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0760-3_3.

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Monnet, Xavier, and Jean–Louis Teboul. "Dynamic Tests." In Advanced Hemodynamic Monitoring: Basics and New Horizons, 161–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71752-0_17.

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Monnet, Xavier, and Jean–Louis Teboul. "Dynamic Indices." In Advanced Hemodynamic Monitoring: Basics and New Horizons, 149–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71752-0_16.

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Giraud, Raphael, and Karim Bendjelid. "Preload Dependency Dynamic Indices." In Hemodynamic Monitoring in the ICU, 75–89. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29430-8_6.

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Chakroun, Ala Eddin, Ahmed Hammami, Ana De-Juan, Fakher Chaari, Alfonso Fernandez, Fernando Viadero, and Mohamed Haddar. "A New Dynamic Model for Worm Drives." In Applied Condition Monitoring, 235–42. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76517-0_26.

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Rezaiguia, Abdelouahab, Oussama Zerti, Salah Guenfoud, and Debra F. Laefer. "Dynamic Behavior and Stability of a Flexible Rotor." In Applied Condition Monitoring, 37–50. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94616-0_4.

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Conference papers on the topic "Dynamic monitoring"

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Rechsteiner, Martin, Bruno Schneuwly, and Gerhard Troester. "Dynamic workspace monitoring." In Spatial Information from Digital Photogrammetry and Computer Vision: ISPRS Commission III Symposium, edited by Heinrich Ebner, Christian Heipke, and Konrad Eder. SPIE, 1994. http://dx.doi.org/10.1117/12.182816.

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BOLDYREV, GENNADII, and ALEKSANDR ZHIVAEV. "Dynamic Monitoring of Nuclear Reactor Protection Shell." In Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/41.

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Murzina, Marina V. A., and J. Paul Farrell. "Dynamic hyperspectral imaging." In Nondestructive Evaulation for Health Monitoring and Diagnostics, edited by Aaron A. Diaz, A. Emin Aktan, H. Felix Wu, Steven R. Doctor, and Yoseph Bar-Cohen. SPIE, 2005. http://dx.doi.org/10.1117/12.598620.

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NICHIFOR, ALEXANDRU, and YONGZHI QU. "KOOPMAN OPERATOR BASED FAULT DIAGNOSTIC METHODS FOR MECHANICAL SYSTEMS." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36299.

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Traditionally, dynamical systems can be simulated with physics-based model when the design parameters and material property are pre-known. However, when a system is deployed in field and has suffered potential degradation, a physics-based model might be infeasible to obtain. Moreover, the non-linearity and unknown coupling between the system and contacting constraints are often hard to determine accurately. The analysis of those systems becomes practically problematic. In this paper, the Koopman operator is used to learn and represent a dynamic system in a data driven manner. This paper proposes two methods of using the Koopman operator to extract and classify critical parameters of a non-linear dynamic mechanical system for fault diagnosis. The first method proposes a model to extract key features from a dynamic system and feed the features to a neural network to classify the existence of a fault. The second method uses parameters derived from the Koopman operator to create a prediction model with healthy data. This prediction model is then used to predict future system dynamics for a measured time evolution and compare that with direct measurements when future dynamics become available. Both methods are then tested via an experimental case study and the results are discussed.
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SHEN, YANFENG, HUI ZHANG, and CARLOS CESNIK. "CLoVER Transducers for Static and Dynamic Strain Sensing." In Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/373.

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Kruger, Uwe, Richard J. Treasure, and Grigorios Dimitriadis. "Subspace Monitoring of Multivariate Dynamic Systems." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58587.

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In this article, the monitoring of continuous processes using linear dynamic models is presented. It is outlined that dynamic extensions to conventional multivariate statistical process control (MSPC) models may lead to the inclusion of large numbers of variables in the condition monitor. To prevent this, a new dynamic monitoring scheme, based on subspace identification, is introduced, which can (i) determine a set of state variable for describing process dynamics and (ii) produce a reduced set of variables to monitor process performance. This is demonstrated by an application study to a realistic simulation of a chemical process.
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BLASCH, ERIK, JAIMIE TILEY, MARTIN SCHMIDT, and GERNOT POMRENKE. "Dynamic Data Driven Applications Systems (DDDAS) for Structural Awareness." In Structural Health Monitoring 2019. Lancaster, PA: DEStech Publications, Inc., 2019. http://dx.doi.org/10.12783/shm2019/32299.

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HABTOUR, ED, DANIEL COLE, ABHIJIT DASGUPTA, and MARK ROBESON. "Damage Precursor Detection Using Nonlinear Dynamic Parameters and Micromechanics." In Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/159.

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MORADI, SARVIN, SAEED (YASHAR) EFTEKHAR AZAM, and MASSOOD MOFID. "PHYSICS-INFORMED NEURAL NETWORK APPROACH FOR IDENTIFICATION OF DYNAMIC SYSTEMS." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36352.

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In this study, a novel method for online and real-time identification of dynamic systems is presented. This method is based on the newly introduced algorithm Physics Informed Neural Network (PINN). In order to find the dynamic characteristics of the system, sparse displacement measurements are fed to the Artificial Neural Network (ANN); By introducing the classic vibration equation of the system to the ANN as a physics constraint, the PINN estimates both dynamic characteristic and state of the system. The proposed framework is evaluated by several numerical studies with different system properties, noise levels, architecture, and training data. On that account, four structural systems are presented: (1) single-degree-of-freedom (SDOF) systems with different properties and noise levels, as basis model with an accurate analytical solution (2) a three-degree-of-freedom (3-DOF) system with both complete and sparse measurements, representing the structural model of the n-story shear frames (3) a simple supported beam subjected to an initial displacement with several NNs architecture and sensor numbers, and (4) a Pure Cubic Oscillator (PCO) as a nonlinear dynamic system. The results of the proposed platform for the PINN are compared to a mutual ANN in all cases to emphasize the superiority of the PINN in both determining the dynamic characteristics and state estimation of dynamic systems. In addition, the performance of both NNs is examined with different training data to ensure the resilience of the algorithm and affirm the role of the added criteria, physics constraint, in reducing the dependency on the training data. The proposed algorithm can accurately estimate the dynamic characteristics of different dynamic systems with sparse, noisy measurements; by means of the classic dynamic equations and smartly selection of the hidden layer numbers, the PINN will be a powerful predictive tool for the dynamic analysis in the absence of any prior knowledge of the dynamic systems.
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Amsters, Robin, Ali Bin Junaid, Nick Damen, Jeroen Van de Laer, Benjamin Filtjens, Bart Vanrumste, and Peter Slaets. "Dynamic Gait Monitoring Mobile Platform." In 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006733200490061.

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Reports on the topic "Dynamic monitoring"

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Kang, Byeong Ho, Paul Compton, Hiroshi Motoda, and John Salerno. Dynamic Scheduling for Web Monitoring Crawler. Fort Belvoir, VA: Defense Technical Information Center, February 2009. http://dx.doi.org/10.21236/ada494589.

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Merl, D. M. Network Traffic Monitoring Using Poisson Dynamic Linear Models. Office of Scientific and Technical Information (OSTI), May 2011. http://dx.doi.org/10.2172/1122210.

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Worsey, Matthew T. O., Regina Arias, David V. Thiel, Jonathan Shepherd, Allan Hahn, and Hugo G. Espinosa. Monitoring Balance in Rowing Using a Modified Dynamic Ergometer. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317475.

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Everhart-Erickson, Michael Charles. Video-Based Dynamic Measurement & Analysis for Structural Health Monitoring. Office of Scientific and Technical Information (OSTI), April 2018. http://dx.doi.org/10.2172/1435550.

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Clarke, Amy J. Early Career: In-situ Monitoring of Dynamic Phenomena during Solidification. Office of Scientific and Technical Information (OSTI), June 2019. http://dx.doi.org/10.2172/1524788.

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Carlson, Jeffrey J. Automated video screening for unattended background monitoring in dynamic environments. Office of Scientific and Technical Information (OSTI), March 2004. http://dx.doi.org/10.2172/1029800.

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Meliopoulos, Sakis, George Cokkinides, Bruce Fardanesh, and Clinton Hedrington. Distributed Dynamic State Estimator, Generator Parameter Estimation and Stability Monitoring Demonstration. Office of Scientific and Technical Information (OSTI), December 2013. http://dx.doi.org/10.2172/1176943.

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Ghoshal, Sudipto, Mohammad Azam, and Sunil Dixit. Dynamic Data-Driven Prognostics and Condition Monitoring of On-board Electronics. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada585476.

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Sutkoff, David, Jerilyn McElwee, and Brad Gaspard. Software Test Report for the Network Monitoring and Management Hybrid MANET Dynamic Addressing. Fort Belvoir, VA: Defense Technical Information Center, July 2007. http://dx.doi.org/10.21236/ada474627.

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Seiple, Jacqueline, Christopher Spaur, Safra Altman, Matthew Balazik, Luis Santiago, Daniel Mensah, Warunika Amarasingha, Steven Golder, and Andrew Payson. Post-project monitoring of a navigation solution in a dynamic coastal environment, Smith Island, Maryland : year one of post-project monitoring. Engineer Research and Development Center (U.S.), July 2020. http://dx.doi.org/10.21079/11681/37573.

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