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167 results for “ALARMES”
Quality Improvement - Monitoring Alarm Optimization Study
ClinicalTrials.gov study NCT02916212. IPD Sharing: Not stated. Countries: 0. Publications: 0.
ARC Code TI: Optimal Alarm System Design and Implementation
An optimal alarm system can robustly predict a level-crossing event that is specified over a fixed prediction horizon. The code contained in this packages provides the tools necessary to design an optimal alarm system for a simple stationary linear dynamic system driven by white Gaussian noise.
Optimal Alarm Systems
An optimal alarm system is simply an optimal level-crossing predictor that can be designed to elicit the fewest false alarms for a fixed detection probability. It currently use Kalman filtering for dynamic systems to provide a layer of predictive capability for the forecasting of adverse events. Predicted Kalman filter future process values and a fixed critical threshold can be used to construct a candidate level-crossing event over a predetermined prediction window. Due to the fact that the alarm regions for an optimal level-crossing predictor cannot be expressed in closed form, one of our aims has been to investigate approximations for the design of an optimal alarm system. Approximations to this sort of alarm region are required for the most computationally efficient generation of a ROC curve or other similar alarm system design metrics. Algorithms based upon the optimal alarm system concept also require models that appeal to a variety of data mining and machine learning techniques. As such, we have investigated a serial architecture which was used to preprocess a full feature space by using SVR (Support Vector Regression), implicitly reducing it to a univariate signal while retaining salient dynamic characteristics (see AIAA attachment below). This step was required due to current technical constraints, and is performed by using the residual generated by SVR (or potentially any regression algorithm) that has properties which are favorable for use as training data to learn the parameters of a linear dynamical system. Future development will lift these restrictions so as to allow for exposure to a broader class of models such as a switched multi-input/output linear dynamical system in isolation based upon heterogeneous (both discrete and continuous) data, obviating the need for the use of a preprocessing regression algorithm in serial. However, the use of a preprocessing multi-input/output nonlinear regression algorithm in serial with a multi-input/output linear dynamical system will allow for the characterization of underlying static nonlinearities to be investigated as well. We will even investigate the use of non-parametric methods such as Gaussian process regression and particle filtering in isolation to lift the linear and Gaussian assumptions which may be invalid for many applications. Future work will also involve improvement of approximations inherent in use of the optimal alarm system of optimal level-crossing predictor. We will also perform more rigorous testing and validation of the alarm systems discussed by using standard machine learning techniques and consider more complex, yet practically meaningful critical level-crossing events. Finally, a more detailed investigation of model fidelity with respect to available data and metrics has been conducted (see attachment below). As such, future work on modeling will involve the investigation of necessary improvements in initialization techniques and data transformations for a more feasible fit to the assumed model structure. Additionally, we will explore the integration of physics-based and data-driven methods in a Bayesian context, by using a more informative prior.
Responses of various cells in the zebrafish olfactory epithelium after treatment with alarm substances
GEO Series GSE283486. Danio rerio. 2 samples. Type: Expression profiling by high throughput sequencing.
Alarm pheromone habituation in Myzus persicae: Fitness consequences and the transcriptome of fear
GEO Series GSE20445. Myzus persicae. 16 samples. Type: Expression profiling by array.
ALARMES PANTANAL HISTORIC
<p>Results from burned area product over Brazilian wetlands (Pantanal) derived from ALARMES developed on Laboratory for Environmental Satellite Applications (LASA) based in Federal University of Rio de Janeiro (UFRJ) from 2012 through 2021. For additional information about these results, please see the <a href="https://lasa.ufrj.br/alarmes/">LASA website</a>. </p>
Data from "Revisiting the burglar-alarm hypothesis: a behavioral cascade mediated by dinoflagellate bioluminescence"
<p>Data used in the manuscript "Revisiting the burglar-alarm hypothesis: a behavioral cascade mediated by dinoflagellate bioluminescence". </p> <p>Data includes nauplii of the copepod Temora longicornis grazing on three species of bioluminescent dinoflagellates, <em>Alexandrium tamarense</em>, <em>Lingulodinium polyedra</em>, and <em>Protoceratium reticulatum; </em>data on the jumping behavior of the same nauplii when exposed to these dinoflagellates during their flashing- and non-flashing phases; and data on the grazing of the copepod <em>Centropages typicus</em> on the <em>T. longicornis </em>nauplii in a suspension of flashing- or non-flashing dinoflagellate cells. </p>
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