Relevant bibliographies by topics / Sampled-data models

Academic literature on the topic 'Sampled-data models'

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Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "Sampled-data models"

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Goodwin, Graham C., Juan I. Yuz, and Juan C. Agüero. "Relative Error Issues in Sampled Data Models." IFAC Proceedings Volumes 41, no. 2 (2008): 5047–52. http://dx.doi.org/10.3182/20080706-5-kr-1001.00848.

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Yuz, Juan I., and Graham C. Goodwin. "SAMPLED-DATA MODELS FOR STOCHASTIC NONLINEAR SYSTEMS." IFAC Proceedings Volumes 39, no. 1 (2006): 434–39. http://dx.doi.org/10.3182/20060329-3-au-2901.00065.

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Yiin, Lih-Huah, and H. Vincent Poor. "Linear interpolation models for rapidly-sampled data." Communications in Statistics - Theory and Methods 14, no. 4 (1998): 867–82. http://dx.doi.org/10.1080/03610929808828953.

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Yuz, J. I., and G. C. Goodwin. "On sampled-data models for nonlinear systems." IEEE Transactions on Automatic Control 50, no. 10 (October 2005): 1477–89. http://dx.doi.org/10.1109/tac.2005.856640.

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Yiin, Lih-Huah, and H. Vincent Poor. "Linear interpolation models for rapidly-sampled data." Communications in Statistics. Stochastic Models 14, no. 4 (January 1998): 867–82. http://dx.doi.org/10.1080/15326349808807505.

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Radchenko, Peter, Xinghao Qiao, and Gareth M. James. "Index Models for Sparsely Sampled Functional Data." Journal of the American Statistical Association 110, no. 510 (April 3, 2015): 824–36. http://dx.doi.org/10.1080/01621459.2014.931859.

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Yucra, Eduardo A., and Juan I. Yuz. "Frequency domain accuracy of approximate sampled-data models." IFAC Proceedings Volumes 44, no. 1 (January 2011): 8711–17. http://dx.doi.org/10.3182/20110828-6-it-1002.02257.

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Moheimani, S. O. Reza. "Model Correction for Sampled-Data Models of Structures." Journal of Guidance, Control, and Dynamics 24, no. 3 (May 2001): 634–37. http://dx.doi.org/10.2514/2.4760.

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Rabbath, C. A., N. Hori, and N. Lechevin. "Convergence of Sampled-Data Models in Digital Redesign." IEEE Transactions on Automatic Control 49, no. 5 (May 2004): 850–55. http://dx.doi.org/10.1109/tac.2004.828312.

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Wang, Jiandong, Tongwen Chen, and Biao Huang. "Multirate sampled-data systems: computing fast-rate models." Journal of Process Control 14, no. 1 (February 2004): 79–88. http://dx.doi.org/10.1016/s0959-1524(03)00033-7.

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Dissertations / Theses on the topic "Sampled-data models"

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Findeisen, Rolf. "Nonlinear model predictive control a sampled data feedback perspective /." [S.l. : s.n.], 2004.

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Chen, Fengwei. "Contributions à l'identification de modèles à temps continu à partir de données échantillonnées à pas variable." Thesis, Université de Lorraine, 2014. http://www.theses.fr/2014LORR0149/document.

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Cette thèse traite de l’identification de systèmes dynamiques à partir de données échantillonnées à pas variable. Ce type de données est souvent rencontré dans les domaines biomédical, environnemental, dans le cas des systèmes mécaniques où un échantillonnage angulaire est réalisé ou lorsque les données transitent sur un réseau. L’identification directe de modèles à temps continu est l’approche à privilégier lorsque les données disponibles sont échantillonnées à pas variable ; les paramètres des modèles à temps discret étant dépendants de la période d’échantillonnage. Dans une première partie, un estimateur optimal de type variable instrumentale est développé pour estimer les paramètres d’un modèle Box-Jenkins à temps continu. Ce dernier est itératif et présente l’avantage de fournir des estimées non biaisées lorsque le bruit de mesure est coloré et sa convergence est peu sensible au choix du vecteur de paramètres initial. Une difficulté majeure dans le cas où les données sont échantillonnées à pas variable concerne l’estimation de modèles de bruit de type AR et ARMA à temps continu (CAR et CARMA). Plusieurs estimateurs pour les modèles CAR et CARMA s’appuyant sur l’algorithme Espérance-Maximisation (EM) sont développés puis inclus dans l’estimateur complet de variable instrumentale optimale. Une version étendue au cas de l’identification en boucle fermée est également développée. Dans la deuxième partie de la thèse, un estimateur robuste pour l'identification de systèmes à retard est proposé. Cette classe de systèmes est très largement rencontrée en pratique et les méthodes disponibles ne peuvent pas traiter le cas de données échantillonnées à pas variable. Le retard n’est pas contraint à être un multiple de la période d’échantillonnage, contrairement à l’hypothèse traditionnelle dans le cas de modèles à temps discret. L’estimateur développé est de type bootstrap et combine la méthode de variable instrumentale itérative pour les paramètres de la fonction de transfert avec un algorithme numérique de type gradient pour estimer le retard. Un filtrage de type passe-bas est introduit pour élargir la région de convergence pour l’estimation du retard. Tous les estimateurs proposés sont inclus dans la boîte à outils logicielle CONTSID pour Matlab et sont évalués à l’aide de simulation de Monte-Carlo
The output of a system is always corrupted by additive noise, therefore it is more practical to develop estimation algorithms that are capable of handling noisy data. The effect of white additive noise has been widely studied, while a colored additive noise attracts less attention, especially for a continuous-time (CT) noise. Sampling issues of CT stochastic processes are reviewed in this thesis, several sampling schemes are presented. Estimation of a CT stochastic process is studied. An expectation-maximization-based (EM) method to CT autoregressive/autoregressive moving average model is developed, which gives accurate estimation over a large range of sampling interval. Estimation of CT Box-Jenkins models is also considered in this thesis, in which the noise part is modeled to improve the performance of plant model estimation. The proposed method for CT Box-Jenkins model identification is in a two-step and iterative framework. Two-step means the plant and noise models are estimated in a separate and alternate way, where in estimating each of them, the other is assumed to be fixed. More specifically, the plant is estimated by refined instrumental variable (RIV) method while the noise is estimated by EM algorithm. Iterative means that the proposed method repeats the estimation procedure several times until a optimal estimate is found. Many practical systems have inherent time-delay. The problem of identifying delayed systems are of great importance for analysis, prediction or control design. The presence of a unknown time-delay greatly complicates the parameter estimation problem, essentially because the model are not linear with respect to the time-delay. An approach to continuous-time model identification of time-delay systems, combining a numerical search algorithm for the delay with the RIV method for the dynamic has been developed in this thesis. In the proposed algorithm, the system parameters and time-delay are estimated reciprocally in a bootstrap manner. The time-delay is estimated by an adaptive gradient-based method, whereas the system parameters are estimated by the RIV method. Since numerical method is used in this algorithm, the bootstrap method is likely to converge to local optima, therefore a low-pass filter has been used to enlarge the convergence region for the time-delay. The performance of the proposed algorithms are evaluated by numerical examples
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Yuz, Eissmann Juan Ignacio. "Sampled-data models for linear and nonlinear systems." Thesis, 2006. http://hdl.handle.net/1959.13/24852.

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Continuous-time systems are usually modelled by differential equations arising from physical laws. However, the use of these models in practice requires discretisation. In this thesis we consider sampled-data models for linear and nonlinear systems. We study some of the issues involved in the sampling process, such as the accuracy of the sampled-data models, the artifacts produced by the particular sampling scheme, and the relations to the underlying continuous-time system. We review, extend and present new results, making extensive use of the delta operator which allows a clearer connection between a sampled-data model and the underlying continuous-time system. In the first part of the thesis we consider sampled-data models for linear systems. In this case exact discrete-time representations can be obtained. These models depend, not only on the continuous-time system, but also on the artifacts involved in the sampling process, namely, the sample and hold devices. In particular, these devices play a key role in determining the sampling zeros of the discrete-time model. We consider robustness issues associated with the use of discrete-time models for continuous-time system identification from sampled data. We show that, by using restricted bandwidth frequency domain maximum likelihood estimation, the identification results are robust to (possible) under-modelling due to the sampling process. Sampled-data models provide a powerful tool also for continuous-time optimal control problems, where the presence of constraints can make the explicit solution impossible to find. We show how this solution can be arbitrarily approximated by an associated sampled-data problem using fast sampling rates. We also show that there is a natural convergence of the singular structure of the optimal control problem from discrete- to continuous-time, as the sampling period goes to zero. In Part II we consider sampled-data models for nonlinear systems. In this case we can only obtain approximate sampled-data models. These discrete-time models are simple and accurate in a well defined sense. For deterministic systems, an insightful observation is that the proposed model contains sampling zero dynamics. Moreover, these correspond to the same dynamics associated with the asymptotic sampling zeros in the linear case. The topics and results presented in the thesis are believed to give important insights into the use of sampled-data models to represent linear and nonlinear continuous-time systems.
PhD Doctorate
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Yuz, Eissmann Juan Ignacio. "Sampled-data models for linear and nonlinear systems." 2006. http://hdl.handle.net/1959.13/24852.

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Continuous-time systems are usually modelled by differential equations arising from physical laws. However, the use of these models in practice requires discretisation. In this thesis we consider sampled-data models for linear and nonlinear systems. We study some of the issues involved in the sampling process, such as the accuracy of the sampled-data models, the artifacts produced by the particular sampling scheme, and the relations to the underlying continuous-time system. We review, extend and present new results, making extensive use of the delta operator which allows a clearer connection between a sampled-data model and the underlying continuous-time system. In the first part of the thesis we consider sampled-data models for linear systems. In this case exact discrete-time representations can be obtained. These models depend, not only on the continuous-time system, but also on the artifacts involved in the sampling process, namely, the sample and hold devices. In particular, these devices play a key role in determining the sampling zeros of the discrete-time model. We consider robustness issues associated with the use of discrete-time models for continuous-time system identification from sampled data. We show that, by using restricted bandwidth frequency domain maximum likelihood estimation, the identification results are robust to (possible) under-modelling due to the sampling process. Sampled-data models provide a powerful tool also for continuous-time optimal control problems, where the presence of constraints can make the explicit solution impossible to find. We show how this solution can be arbitrarily approximated by an associated sampled-data problem using fast sampling rates. We also show that there is a natural convergence of the singular structure of the optimal control problem from discrete- to continuous-time, as the sampling period goes to zero. In Part II we consider sampled-data models for nonlinear systems. In this case we can only obtain approximate sampled-data models. These discrete-time models are simple and accurate in a well defined sense. For deterministic systems, an insightful observation is that the proposed model contains sampling zero dynamics. Moreover, these correspond to the same dynamics associated with the asymptotic sampling zeros in the linear case. The topics and results presented in the thesis are believed to give important insights into the use of sampled-data models to represent linear and nonlinear continuous-time systems.
PhD Doctorate
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Ramroop, Shaun. "An approach to estimating the variance components to unbalanced cluster sampled survey data and simulated data." Diss., 2002. http://hdl.handle.net/10500/762.

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"Modelling irregularly sampled time series : an application on Hong Kong water pollution data." Chinese University of Hong Kong, 1986. http://library.cuhk.edu.hk/record=b5885655.

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CHEN, CHENG-LIANG, and 陳誠亮. "Identification of continuous-time models for linar multivariable dynamic systems via sampled data." Thesis, 1987. http://ndltd.ncl.edu.tw/handle/91097850936882099575.

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"Linear averaged and sampled data models for large signal control of high power factor Ac-DC converters." Massachusetts Institute of Technology, Laboratory for Information and Decision Systems], 1990. http://hdl.handle.net/1721.1/3198.

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K. Mahabir ... [et al.].
Cover title.
Includes bibliographical references (leaf 9).
Work partially supported by DEC. Work partially supported by the Air Force Office of Scientific Research. AFOSR-88-0032 Work partially supported by the MIT/Industry Power Electronics Collegium.
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Carrasco, Yanez Diego S. "Uncertainty issues in deterministic and stochastic nonlinear systems." Thesis, 2014. http://hdl.handle.net/1959.13/1049172.

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Research Doctorate - Doctor of Philosophy (PhD)
Robustness issues arise in every real world control problem. The objective of any robust control strategy is to preserve closed-loop stability in situations where the real plant differs from the model used to design the controller, i.e. the real system is, in some sense, unknown. There are different ways to quantify, or describe, the uncertainty of a model. It is the amount of uncertainty, or lack of confidence in the model, that ultimately determines, and constrains, what the closed-loop can achieve. In this thesis we address particular issues concerned with how to quantify and reduce the impact of uncertainty. To this end, the present thesis is divided in two parts: The first part is aimed at linear systems. We propose two ideas on how to improve closed-loop performance in the face of general uncertainty, namely, (i) augmenting the control architecture with a feedforward component and (ii) augmenting the observer architecture by using the more general class of unbiased observers. We then illustrate the first strategy applied to an Artificial Pancreas problem. The second part is aimed at nonlinear systems. A common source of uncertainty in this area is the use of approximate sampled-data models of continuous time systems, be it for control design or system identifcation. This is due to the fact that, contrary to the linear case, exact discretisations are not generally possible in the nonlinear case. In particular, we deal with the sampled-data scenario in both deterministic and stochastic cases and focus our attention on accuracy and related properties of sampled-data models. We first study the accuracy properties, or error dynamics, of a particular deterministic sampled data model, and show that it possesses an improved order of accuracy when compared to the usual Euler approximation. We then demonstrate the usefulness of having such a quantification via several applications, namely, (i) obtaining better bias-variance tradeoffs in the parameter estimation of continuous-time systems from sampled-data, (ii) obtaining a sampled-data model that depends only on input-output data that retains the improved order of accuracy, and (iii) obtaining better performance in high-gain sampled-data feedback control of nonlinear systems, via feedback lineraisation. In addition, we extend the analysis to stochastic sampled-data nonlinear systems. In this case, we show that the error dynamics are tightly intertwined with other system properties that arise due to the sampling process. In particular, we show the existence of stochastic sampling zero dynamics that are closely related to the sampling zero dynamics associated with the deterministic case.
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Findeisen, Rolf [Verfasser]. "Nonlinear model predictive control: a sampled-data feedback perspective / vorgelegt von Rolf Findeisen." 2005. http://d-nb.info/979741750/34.

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Books on the topic "Sampled-data models"

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Hugues, Garnier, and Wang Liuping, eds. Identification of continuous-time models from sampled data. London: Springer, 2008.

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Yuz, Juan I., and Graham C. Goodwin. Sampled-Data Models for Linear and Nonlinear Systems. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5562-1.

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Garnier, Hugues, and Liuping Wang, eds. Identification of Continuous-time Models from Sampled Data. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-161-9.

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Ghysels, Eric. Predicitng volatility: Getting the most out of return data sampled at different frequencies. Cambridge, MA: National Bureau of Economic Research, 2004.

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Ghysels, Eric. Predicting volatility: Getting the most out of return data sampled at different frequencies. Cambridge, Mass: National Bureau of Economic Research, 2004.

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Tsang, K. M. Reconstruction of linear and nonlinear continuous time models from discrete time sampled-data systems. Sheffield: University of Sheffield, Dept. of Control Engineering, 1990.

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Byun, Jae-Woong. Estimation of discrete dynamic models from endogenously-sampled company panel data: An analysis of direct investmentby Korean firms in the European Union. Leicester: University of Leicester, Department of Economics, 1994.

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Yuz, Juan, and Graham C. Goodwin. Sampled-Data Models for Linear and Nonlinear Systems. Springer London, Limited, 2016.

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Yuz, Juan I., and Graham C. Goodwin. Sampled-Data Models for Linear and Nonlinear Systems. Springer London, Limited, 2013.

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Wang, Liuping, and Hugues Garnier. Identification of Continuous-Time Models from Sampled Data. Springer London, Limited, 2010.

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Book chapters on the topic "Sampled-data models"

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Yuz, Juan I., and Graham C. Goodwin. "Incremental Sampled-Data Models." In Communications and Control Engineering, 39–45. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5562-1_4.

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Yuz, Juan I., and Graham C. Goodwin. "Incremental Stochastic Sampled-Data Models." In Communications and Control Engineering, 157–67. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5562-1_13.

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Murray-Smith, D. J. "Sampled-Data Models and Operator Methods." In Continuous System Simulation, 67–84. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2504-2_5.

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Yuz, Juan I., and Graham C. Goodwin. "Sampled-Data Models for Linear Stochastic Systems." In Communications and Control Engineering, 149–56. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5562-1_12.

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Yuz, Juan I., and Graham C. Goodwin. "Applications of Approximate Stochastic Sampled-Data Models." In Communications and Control Engineering, 233–50. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5562-1_19.

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Yuz, Juan I., and Graham C. Goodwin. "Sampled-Data Models for Linear Deterministic Systems." In Communications and Control Engineering, 21–38. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5562-1_3.

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Southard, David A. "Piecewise Planar Surface Models from Sampled Data." In Scientific Visualization of Physical Phenomena, 667–80. Tokyo: Springer Japan, 1991. http://dx.doi.org/10.1007/978-4-431-68159-5_37.

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Yuz, Juan I., and Graham C. Goodwin. "Approximate Sampled-Data Models for Linear Stochastic Systems." In Communications and Control Engineering, 195–207. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5562-1_16.

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Yuz, Juan I., and Graham C. Goodwin. "Approximate Sampled-Data Models for Nonlinear Stochastic Systems." In Communications and Control Engineering, 221–31. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5562-1_18.

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Yuz, Juan I., and Graham C. Goodwin. "Approximate Sampled-Data Models for Fractional Order Systems." In Communications and Control Engineering, 271–86. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5562-1_22.

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Conference papers on the topic "Sampled-data models"

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Goodwin, G. C., J. I. Yuz, J. C. Aguero, and M. Cea. "Sampling and sampled-data models." In 2010 American Control Conference (ACC 2010). IEEE, 2010. http://dx.doi.org/10.1109/acc.2010.5531562.

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Silva, Cesar A., and Juan I. Yuz. "On sampled-data models for model predictive control." In IECON 2010 - 36th Annual Conference of IEEE Industrial Electronics. IEEE, 2010. http://dx.doi.org/10.1109/iecon.2010.5674939.

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Li Chai and Xiaodong Zhao. "Sampled Data Model Predictive Control for Step Response Models." In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1714305.

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Nishi, Masatoshi, Mitsuaki Ishitobi, and Sadaaki Kunimatsu. "Nonlinear sampled-data models and zero dynamics." In 2009 International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2009. http://dx.doi.org/10.1109/icnsc.2009.4919304.

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Romano, Rodrigo A., Felipe Pait, and P. Lopes dos Santos. "Obtaining multivariable continuous-time models from sampled data." In 2017 American Control Conference (ACC). IEEE, 2017. http://dx.doi.org/10.23919/acc.2017.7962944.

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Karafyllis, Iasson, Michael Malisoff, and Miroslav Krstic. "Sampled-data feedback stabilization of age-structured chemostat models." In 2015 American Control Conference (ACC). IEEE, 2015. http://dx.doi.org/10.1109/acc.2015.7172045.

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Ishitobi, Mitsuaki, and Masatoshi Nishi. "Zero dynamics of sampled-data models for nonlinear systems." In 2008 American Control Conference (ACC '08). IEEE, 2008. http://dx.doi.org/10.1109/acc.2008.4586653.

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Ishitobi, Mitsuaki, and Sadaaki Kunimatsu. "Zeros of sampled-data models for time delay MIMO systems." In TENCON 2016 - 2016 IEEE Region 10 Conference. IEEE, 2016. http://dx.doi.org/10.1109/tencon.2016.7848687.

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Avila, F., J. I. Yuz, A. Donaire, and J. C. Aguero. "Constrained maximum likelihood estimation for state space sampled-data models." In 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC). IEEE, 2018. http://dx.doi.org/10.1109/icstcc.2018.8540710.

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Nagy, Szabolcs. "Exact reconstruction of HOSVD based TP models from sampled data." In 2009 5th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2009. http://dx.doi.org/10.1109/saci.2009.5136214.

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Reports on the topic "Sampled-data models"

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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, July 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 samples of model input. The first dataset consisted of transmission loss (TL) fields for single realizations of turbulence. The second dataset consisted of average TL fields for 64 realizations of turbulence. Three machine-learning algorithms were applied to each dataset, namely, ensemble decision trees, neural networks, and cluster-weighted models. Observational data come from a long-range (out to 8 km) sound propagation experiment. In comparison to the experimental observations, regression predictions have 5–7 dB in median absolute error. Surrogate data quality depends on an accurate characterization of refractive and scattering conditions. Predictions obtained through a single realization of turbulence agree better with the experimental observations.
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Dutra, Lauren M., Matthew C. Farrelly, Brian Bradfield, Jamie Ridenhour, and Jamie Guillory. Modeling the Probability of Fraud in Social Media in a National Cannabis Survey. RTI Press, September 2021. http://dx.doi.org/10.3768/rtipress.2021.mr.0046.2109.

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Cannabis legalization has spread rapidly in the United States. Although national surveys provide robust information on the prevalence of cannabis use, cannabis disorders, and related outcomes, information on knowledge, attitudes, and beliefs (KABs) about cannabis is lacking. To inform the relationship between cannabis legalization and cannabis-related KABs, RTI International launched the National Cannabis Climate Survey (NCCS) in 2016. The survey sampled US residents 18 years or older via mail (n = 2,102), mail-to-web (n = 1,046), and two social media data collections (n = 11,957). This report outlines two techniques that we used to problem-solve several challenges with the resulting data: (1) developing a model for detecting fraudulent cases in social media completes after standard fraud detection measures were insufficient and (2) designing a weighting scheme to pool multiple probability and nonprobability samples. We also describe our approach for validating the pooled dataset. The fraud prevention and detection processes, predictive model of fraud, and the methods used to weight the probability and nonprobability samples can be applied to current and future complex data collections and analysis of existing datasets.
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Swanson, David, and Celia Hampton-Miller. Drained lakes in Bering Land Bridge National Preserve: Vegetation succession and impacts on loon habitat. National Park Service, January 2023. http://dx.doi.org/10.36967/2296593.

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The northern coastal plain of Bering Land Bridge National Preserve (BELA) lost lakes at an alarming rate over the first two decades of this century, including four lakes over 100 ha in size in 2018-2019 alone. To understand the effects of these lake drainages, we sampled vegetation of these lakes in 2019 (a reconnaissance visit) and 2021 (for the installation of permanent vegetation monitoring plots). We used these data to summarize the changes that occurred in the first three years after drainage, and to create vegetation maps from 3-m resolution satellite images coinciding with the visit dates. We used time series of these satellite images to study the rate of drainage and vegetation colonization on the lakes. We analyzed our existing data from older drained lake basins (estimated to be more than 200 years since drainage) and reviewed the literature on vegetation change in drained lakes to understand the vegetation changes that are likely in the future. Finally, we used a model of lake occupancy by loons developed by Mizel et al. (2021) to predict the effect of the 2018-2019 lake drainages on available loon habitat, using both our detailed maps of the four sampled drained lakes, and also data on all drained lakes over most of northern BELA derived from Landsat satellite images. Our results show that the four study lakes drained early in the summer, before the end of June, in 2018 (3 lakes) and 2019 (one lake). A combination of record warm weather and heavy snowfall made 2018 and 2019 especially favorable for lake drainage: thaw subsidence probably enlarged existing drainage outlet channels from the lakes, and large amounts of spring snowmelt runoff deepened the outlet channels by thermal erosion (the combination of thaw and erosion). Drainage exposed moist loamy sediment on the lake bottoms that was rapidly colonized by plants. Substantial vegetation cover developed by late summer in the same year as lake drainage in one lake, in the first post-drainage summer in a second lake, and during the 2nd year after drainage in the remaining two lakes. The first vegetation communities to develop consisted of just one or two dominant species, notably Eleocharis acicularis (spike rush), Equisetum arvense (horsetail), and/or Tephroseris palustris (mastodon flower). Other important early species were Arctophila fulva (pendant grass) and Rorippa palustris (yellow cress). By year 3, the communities had become more diverse, with significant cover by taller wetland graminoid species, including A. fulva, Eriophorum scheuchzeri, and Carex aquatilis. Frozen soil was observed in most locations on the lakes in July of 2021, suggesting that permafrost was forming on the lake bottoms. Comparison of the three-year trends in vegetation change with data from older lake basins suggest that ultimately most lake basins will develop wet tundra communities dominated by Carex aquatilis and mosses, with various low shrub species on acid, peat-dominated soils and permafrost; however, this process should take several centuries. The loon habitat model suggests that drainage essentially eliminated the potential habitat for Yellow-billed Loons on the four study lakes, because the residuals ponds were too small for Yellow-billed Loons to take flight from. A total of 17 lakes drained in northern BELA in 2018-2019. As a result, the potential Yellow-billed Loon nesting habitat in northern BELA probably decreased by approximately 2%, while habitat for Pacific Loons decreased less, by about 0.6%. Habitat for the more abundant Red-throated Loons probably increased slightly as a result of lake drainage, because of their ability to use the small residual ponds created by lake drainage.
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4

Weissinger, Rebecca. Trends in water quality at Bryce Canyon National Park, water years 2006–2021. Edited by Alice Wondrak Biel. National Park Service, November 2022. http://dx.doi.org/10.36967/2294946.

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The National Park Service collects water-quality samples on a rotating basis at three fixed water-quality stations in Bryce Canyon National Park (NP): Sheep Creek, Yellow Creek, and Mossy Cave Spring. Data collection began at Sheep Creek and Yellow Creek in November 2005 and at Mossy Cave in July 2008. Data on in-situ parameters, fecal-coliform samples, major ions, and nutrients are collected monthly, while trace elements are sampled quarterly. This report analyzes data from the beginning of the period of record for each station through water year 2021 to test for trends over time. Concentrations are also compared to relevant water-quality standards for the State of Utah. Overall, water quality at the park’s monitoring stations continues to be excellent, and park managers have been successful in their goal of maintaining these systems in unimpaired condition. Infrequent but continued Escherichia coli exceedances from trespass livestock at Sheep and Yellow creeks support the need for regular fence maintenance along the park boundary. High-quality conditions may qualify all three sites as Category 1 waters, the highest level of anti-degradation protection provided by the State of Utah. Minimum and maximum air temperatures at the park have increased, while precipitation remains highly variable. Increasing air temperatures have led to increasing water temperatures in Sheep and Yellow creeks. Sheep Creek also had a decrease in flow across several quantiles from 2006 to 2021, while higher flows decreased at Yellow Creek in the same period. Surface flows in these two creeks are likely to be increasingly affected by higher evapotranspiration due to warming air temperatures and possibly decreasing snowmelt runoff as the climate changes. The influx of ancient groundwater in both creek drainages helps sustain base flows at the sites. Mossy Cave Spring, which is sampled close to the spring emergence point, showed less of a climate signal than Sheep and Yellow creeks. In our record, the spring shows a modest increase in discharge, including higher flows at higher air temperatures. An uptick in visitation to Water Canyon and the Mossy Cave Trail has so far not been reflected by changes in water quality. There are additional statistical trends in water-quality parameters at all three sites. However, most of these trends are quite small and are likely ecologically negligible. Some statistical trends may be the result of instrument changes and improvements in quality assurance and quality control over time in both the field sampling effort and the laboratory analyses. Long-term monitoring of water-quality stations at Bryce Canyon NP suggests relatively stable aquatic systems that benefit from protection within the park. To maintain these unimpaired conditions into the future, park managers could consider: Regular fence checks and maintenance along active grazing allotments at the park boundary to protect riparian areas and aquatic systems from trespass livestock. Developing a springs-monitoring program to track changes in springflow at spring emergences to better understand bedrock-aquifer water supplies. These data would also help quantify springflow for use in water-rights hearings. Supporting hydrogeologic investigations to map the extent and flow paths of groundwater aquifers. Working with the State of Utah to develop groundwater-protection zones to protect groundwater aquifers from developments that would affect springs in the park. Prioritizing watershed management with proactive fire risk-reduction practices. Explicitly including watershed protection as a goal in plans for fire management and suppression. Using additional data and analyses to better understand the drivers of trends in water quality and their ecological significance. These could include higher-frequency data to better understand relationships between groundwater, precipitation, and surface flows at the sites. These could also include watershed metrics...
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5

Galili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs, and Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, October 1994. http://dx.doi.org/10.32747/1994.7570549.bard.

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The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the project, to develop and apply the two complementary methods for quality control of agricultural commodities. In Israel: An improved firmness testing system was developed and tested with tropical fruits. The new system included an instrumented fruit-bed of three flexible piezoelectric sensors and miniature electromagnetic hammers, which served as fruit support and low-energy excitation device, respectively. Resonant frequencies were detected for determination of firmness index. Two new acoustic parameters were developed for evaluation of fruit firmness and maturity: a dumping-ratio and a centeroid of the frequency response. Experiments were performed with avocado and mango fruits. The internal damping ratio, which may indicate fruit ripeness, increased monotonically with time, while resonant frequencies and firmness indices decreased with time. Fruit samples were tested daily by destructive penetration test. A fairy high correlation was found in tropical fruits between the penetration force and the new acoustic parameters; a lower correlation was found between this parameter and the conventional firmness index. Improved table-top firmness testing units, Firmalon, with data-logging system and on-line data analysis capacity have been built. The new device was used for the full-scale experiments in the next two years, ahead of the original program and BARD timetable. Close cooperation was initiated with local industry for development of both off-line and on-line sorting and quality control of more agricultural commodities. Firmalon units were produced and operated in major packaging houses in Israel, Belgium and Washington State, on mango and avocado, apples, pears, tomatoes, melons and some other fruits, to gain field experience with the new method. The accumulated experimental data from all these activities is still analyzed, to improve firmness sorting criteria and shelf-life predicting curves for the different fruits. The test program in commercial CA storage facilities in Washington State included seven apple varieties: Fuji, Braeburn, Gala, Granny Smith, Jonagold, Red Delicious, Golden Delicious, and D'Anjou pear variety. FI master-curves could be developed for the Braeburn, Gala, Granny Smith and Jonagold apples. These fruits showed a steady ripening process during the test period. Yet, more work should be conducted to reduce scattering of the data and to determine the confidence limits of the method. Nearly constant FI in Red Delicious and the fluctuations of FI in the Fuji apples should be re-examined. Three sets of experiment were performed with Flandria tomatoes. Despite the complex structure of the tomatoes, the acoustic method could be used for firmness evaluation and to follow the ripening evolution with time. Close agreement was achieved between the auction expert evaluation and that of the nondestructive acoustic test, where firmness index of 4.0 and more indicated grade-A tomatoes. More work is performed to refine the sorting algorithm and to develop a general ripening scale for automatic grading of tomatoes for the fresh fruit market. Galia melons were tested in Israel, in simulated export conditions. It was concluded that the Firmalon is capable of detecting the ripening of melons nondestructively, and sorted out the defective fruits from the export shipment. The cooperation with local industry resulted in development of automatic on-line prototype of the acoustic sensor, that may be incorporated with the export quality control system for melons. More interesting is the development of the remote firmness sensing method for sealed CA cool-rooms, where most of the full-year fruit yield in stored for off-season consumption. Hundreds of ripening monitor systems have been installed in major fruit storage facilities, and being evaluated now by the consumers. If successful, the new method may cause a major change in long-term fruit storage technology. More uses of the acoustic test method have been considered, for monitoring fruit maturity and harvest time, testing fruit samples or each individual fruit when entering the storage facilities, packaging house and auction, and in the supermarket. This approach may result in a full line of equipment for nondestructive quality control of fruits and vegetables, from the orchard or the greenhouse, through the entire sorting, grading and storage process, up to the consumer table. The developed technology offers a tool to determine the maturity of the fruits nondestructively by monitoring their acoustic response to mechanical impulse on the tree. A special device was built and preliminary tested in mango fruit. More development is needed to develop a portable, hand operated sensing method for this purpose. In North Carolina: Analysis method based on an Auto-Regressive (AR) model was developed for detecting the first resonance of fruit from their response to mechanical impulse. The algorithm included a routine that detects the first resonant frequency from as many sensors as possible. Experiments on Red Delicious apples were performed and their firmness was determined. The AR method allowed the detection of the first resonance. The method could be fast enough to be utilized in a real time sorting machine. Yet, further study is needed to look for improvement of the search algorithm of the methods. An impact contact-pressure measurement system and Neural Network (NN) identification method were developed to investigate the relationships between surface pressure distributions on selected fruits and their respective internal textural qualities. A piezoelectric dot-matrix pressure transducer was developed for the purpose of acquiring time-sampled pressure profiles during impact. The acquired data was transferred into a personal computer and accurate visualization of animated data were presented. Preliminary test with 10 apples has been performed. Measurement were made by the contact-pressure transducer in two different positions. Complementary measurements were made on the same apples by using the Firmalon and Magness Taylor (MT) testers. Three-layer neural network was designed. 2/3 of the contact-pressure data were used as training input data and corresponding MT data as training target data. The remaining data were used as NN checking data. Six samples randomly chosen from the ten measured samples and their corresponding Firmalon values were used as the NN training and target data, respectively. The remaining four samples' data were input to the NN. The NN results consistent with the Firmness Tester values. So, if more training data would be obtained, the output should be more accurate. In addition, the Firmness Tester values do not consistent with MT firmness tester values. The NN method developed in this study appears to be a useful tool to emulate the MT Firmness test results without destroying the apple samples. To get more accurate estimation of MT firmness a much larger training data set is required. When the larger sensitive area of the pressure sensor being developed in this project becomes available, the entire contact 'shape' will provide additional information and the neural network results would be more accurate. It has been shown that the impact information can be utilized in the determination of internal quality factors of fruit. Until now,
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