ESAI-CEU-UCH solution for American Epilepsy Society Seizure Prediction ChallengeFrancisco Zamora-Martinez
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Presentation given at Cyient Insights (Hyderabad, India).
This work presents the solution proposed by Universidad CEU Cardenal Herrera (ESAI-CEU-UCH) at Kaggle American Epilepsy Society Seizure Prediction Challenge. The proposed solution was positioned as 4th at Kaggle competition.
Different kind of input features (different preprocessing pipelines) and different statistical models are being proposed. This diversity was motivated to improve model combination result.
It is important to note that any of the proposed systems use test set for calibration. The competition allow to do this model calibration using test set, but doing it will reduce the reproducibility of the results in a real world implementation.
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I have made an attempt to make calcification of different activities from Hear Rate Variability (HRV) data. I have also used model comparison here. Mostly it is graphics, but I will try to add some more text in future.
Sparse Representation for Fetal QRS Detection in Abdominal ECG RecordingsRiccardo Bernardini
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Slideshow of the presentation given at EHB 2015
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ESAI-CEU-UCH solution for American Epilepsy Society Seizure Prediction ChallengeFrancisco Zamora-Martinez
Ā
Presentation given at Cyient Insights (Hyderabad, India).
This work presents the solution proposed by Universidad CEU Cardenal Herrera (ESAI-CEU-UCH) at Kaggle American Epilepsy Society Seizure Prediction Challenge. The proposed solution was positioned as 4th at Kaggle competition.
Different kind of input features (different preprocessing pipelines) and different statistical models are being proposed. This diversity was motivated to improve model combination result.
It is important to note that any of the proposed systems use test set for calibration. The competition allow to do this model calibration using test set, but doing it will reduce the reproducibility of the results in a real world implementation.
An Illustration from Heart Rate Variability Data
I have made an attempt to make calcification of different activities from Hear Rate Variability (HRV) data. I have also used model comparison here. Mostly it is graphics, but I will try to add some more text in future.
Sparse Representation for Fetal QRS Detection in Abdominal ECG RecordingsRiccardo Bernardini
Ā
Slideshow of the presentation given at EHB 2015
In this work, we consider the problem of detection of fetal heart beats from abdominal, non-invasive mixture recordings. We propose a new method for the separation of maternal and fetal beats based on the sparse decomposition in an over-complete dictionary of Gaussian-like functions. To increase the detection capability, we also use Independent Component Analysis (ICA) after maternal template subtraction. We show that the proposed detection method can be applied on the original mixture with a sensitivity close to 95%. Moreover, our method may be used also for single channel abdominal ECG signals, and also used in real-time applications.
Data Driven Choice of Threshold in Cepstrum Based Spectrum Estimatesipij
Ā
The technique of cepstrum thresholding, which is shown to be an effective, yet simple, way of obtaining a smoothed non parametric spectrum estimate of a stationary signal. The major problem of this method is the choice of the threshold value for variance reduction of spectrum estimates. This paper proposes a new threshold selection method which is based on cross validation schemes such as Leave-One-Out, LeaveTwo-Out and Leave-Half-Out. This new methods are easy to describe, simple to implement, and does not impose severe conditions on the unknown spectrum. Numerical results suggest that this new methods are shown to be in agreement with those obtained when the spectrum is fully known.
Bayesian modelling and computation for Raman spectroscopyMatt Moores
Ā
Raman spectroscopy can be used to identify molecules by the characteristic scattering of light from a laser. Each Raman-active dye label has a unique spectral signature, comprised by the locations and amplitudes of the peaks. The Raman spectrum is discretised into a multivariate observation that is highly collinear, hence it lends itself to a reduced-rank representation. We introduce a sequential Monte Carlo (SMC) algorithm to separate this signal into a series of peaks plus a smoothly-varying baseline, corrupted by additive white noise. By incorporating this representation into a Bayesian functional regression, we can quantify the relationship between dye concentration and peak intensity. We also estimate the model evidence using SMC to investigate long-range dependence between peaks. These methods have been implemented as an R package, using RcppEigen and OpenMP.
PROGRAMMA ATTIVITAā DIDATTICA A.A. 2016/17
DOTTORATO DI RICERCA IN INGEGNERIA STRUTTURALE E GEOTECNICA
____________________________________________________________
STOCHASTIC DYNAMICS AND MONTE CARLO SIMULATION IN EARTHQUAKE ENGINEERING APPLICATIONS
Lecture Series by
Agathoklis Giaralis, Ph.D., M.ASCE., P.E. City, University of London
Visiting Professor Sapienza University of Rome
DEEP LEARNING BASED MULTIPLE REGRESSION TO PREDICT TOTAL COLUMN WATER VAPOR (...IJDKP
Ā
Total column water vapor is an important factor for the weather and climate. This study apply
deep learning based multiple regression to map the TCWV with elements that can improve
spatiotemporal prediction. In this study, we predict the TCWV with the use of ERA5 that is the
fifth generation ECMWF atmospheric reanalysis of the global climate. We use an appropriate
deep learning based multiple regression algorithm using Keras library to improve nonlinear
prediction between Total Column water vapor and predictors as Mean sea level pressure, Surface
pressure, Sea surface temperature, 100 metre U wind component, 100 metre V wind component,
10 metre U wind component, 10 metre V wind component, 2 metre dew point temperature, 2
metre temperature.
33 Measurement of beam-recoil observables Ox, Oz and target asymmetry T for t...Cristian Randieri PhD
Ā
Measurement of beam-recoil observables Ox, Oz and target asymmetry T for the reaction Ī³Ļ ā K+Ī - The European Physical Journal A, Hadrons and Nuclei, February 2009, Vol. 39, N. 2, pp. 149ā161, ISSN: 1434-6001, doi: 10.1140/epja/i2008-10713-4
di A. Lleres, O. Bartalini, V. Bellini, J. P. Bocquet, P. Calvat, M. Capogni, L. Casano, M. Castoldi, A. DāAngelo, J. P. Didelez, R. Di Salvo, A. Fantini, D. Franco, C. Gaulard, G. Gervino, F. Ghio, B. Girolami, A. Giusa, M. Guidal, E. Hourany, R. Kunne, V. Kuznetsov, A. Lapik, P. Levi Sandri, F. Mammoliti, G. Mandaglio, D. Moricciani, A. N. Mushkarenkov, V. Nedorezov, L. Nicoletti, C. Perrin, C. Randieri, D. Rebreyend, F. Renard, N. Rudnev, T. Russew, G. Russo, C. Schaerf, M. L. Sperduto, M. C. Sutera, A. Turinge, V. Vegna (2009)
Abstract
The double polarization (beam-recoil) observables Ox and Oz have been measured for the reac- tion Ī³p ā K+Ī from threshold production to E ā¼ 1500MeV. The data were obtained with the linearly polarized beam of the GRAAL facility. Values for the target asymmetry T could also be extracted despite the use of an unpolarized target. Analyses of our results by two isobar models tend to confirm the necessity to include new or poorly known resonances in the 1900MeV mass region.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
Ā
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3ķ95 ā¤ ķķķøķ. This method can provide an
algorithm reference for the design of automatic calibration equipment.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
Ā
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3ķ95 ā¤ ķķķøķ. This method can provide an
algorithm reference for the design of automatic calibration equipment.
A Non Parametric Estimation Based Underwater Target ClassifierCSCJournals
Ā
Underwater noise sources constitute a prominent class of input signal in most underwater signal processing systems. The problem of identification of noise sources in the ocean is of great importance because of its numerous practical applications. In this paper, a methodology is presented for the detection and identification of underwater targets and noise sources based on non parametric indicators. The proposed system utilizes Cepstral coefficient analysis and the Kruskal-Wallis H statistic along with other statistical indicators like F-test statistic for the effective detection and classification of noise sources in the ocean. Simulation results for typical underwater noise data and the set of identified underwater targets are also presented in this paper.
Diagnosis of Faulty Sensors in Antenna Array using Hybrid Differential Evolut...IJECEIAES
Ā
In this work, differential evolution based compressive sensing technique for detection of faulty sensors in linear arrays has been presented. This algorithm starts from taking the linear measurements of the power pattern generated by the array under test. The difference between the collected compressive measurements and measured healthy array field pattern is minimized using a hybrid differential evolution (DE). In the proposed method, the slow convergence of DE based compressed sensing technique is accelerated with the help of parallel coordinate decent algorithm (PCD). The combination of DE with PCD makes the minimization faster and precise. Simulation results validate the performance to detect faulty sensors from a small number of measurements.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
Ā
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3ķ95 ā¤ ķķķøķ. This method can provide an
algorithm reference for the design of automatic calibration equipment.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
Ā
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3š95 ā¤ šššøš. This method can provide an
algorithm reference for the design of automatic calibration equipment.
Evolutionary generation and degeneration of randomness to assess the independ...David F. Barrero
Ā
Randomness tests are a key tool to assess the quality of pseudo-random and true random (physical) number generators. They exploit some properties of random numbers to quantify to which extent the observed behavior of the tested sequence approximates the expected one. Given the many sides of randomness, there is not an unique test providing the whole picture, it is needed instead a suite of tests assessing different aspects randomness. A robust test suite must include independent tests, otherwise tests would assess the same property, providing redundant information. This paper addresses the independence assessment of a popular test suite named Ent. To this end we generate a large number of pseudo-random numbers with different degrees of randomness by evolving them with a Genetic Algorithm. The numbers are generated to maximize their diversity attending different criteria based on Ent output, used as fitness. We encourage diversity by maximizing and minimizing randomness measures. Once a diverse set of pseudo-random numbers is generated, the Ent test suite is run on them, and their statistics studied by means of a classical correlation analysis. The results show high correlation among some statistics used in the literature, which could be overestimating the quality of their randomness source.
Uncertainty in Petrophysics From Bayes To Monte CarloSimon Stromberg
Ā
Increasingly, reduced petrophysical data sets are being used to make critical decisions about the location of deepwater exploration wells. In addition, while drilling deepwater wells, reduced data sets are being used to make well design and completion decisions that may impact the safe operation of the well. However, despite numerous examples in the literature of how data is used to make decisions, very little attention has been given to reliability of the data and its impact on uncertainty, diagnosis, and risk. This presentation will act as a catalyst for starting a discussion on the impact of data reliability on risk assessment. The talk will cover the fundamental principles of uncertainty risk and the impact of data reliability on decision making. Two examples will be used to illustrate the issues. The first will look at risking of exploration wells using AVO response that has low/marginal reliability. The second example will be the risk analysis of a published paper: āDetecting Shallow Drilling Hazard in Large Boreholes Using LWD Acousticsā. This paper avoided the issue of data reliability. The potential impact of not considering data reliability will be explored and discussed with the group.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Ā
Francesca Gottschalk from the OECDās Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
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Data Driven Choice of Threshold in Cepstrum Based Spectrum Estimatesipij
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The technique of cepstrum thresholding, which is shown to be an effective, yet simple, way of obtaining a smoothed non parametric spectrum estimate of a stationary signal. The major problem of this method is the choice of the threshold value for variance reduction of spectrum estimates. This paper proposes a new threshold selection method which is based on cross validation schemes such as Leave-One-Out, LeaveTwo-Out and Leave-Half-Out. This new methods are easy to describe, simple to implement, and does not impose severe conditions on the unknown spectrum. Numerical results suggest that this new methods are shown to be in agreement with those obtained when the spectrum is fully known.
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Raman spectroscopy can be used to identify molecules by the characteristic scattering of light from a laser. Each Raman-active dye label has a unique spectral signature, comprised by the locations and amplitudes of the peaks. The Raman spectrum is discretised into a multivariate observation that is highly collinear, hence it lends itself to a reduced-rank representation. We introduce a sequential Monte Carlo (SMC) algorithm to separate this signal into a series of peaks plus a smoothly-varying baseline, corrupted by additive white noise. By incorporating this representation into a Bayesian functional regression, we can quantify the relationship between dye concentration and peak intensity. We also estimate the model evidence using SMC to investigate long-range dependence between peaks. These methods have been implemented as an R package, using RcppEigen and OpenMP.
PROGRAMMA ATTIVITAā DIDATTICA A.A. 2016/17
DOTTORATO DI RICERCA IN INGEGNERIA STRUTTURALE E GEOTECNICA
____________________________________________________________
STOCHASTIC DYNAMICS AND MONTE CARLO SIMULATION IN EARTHQUAKE ENGINEERING APPLICATIONS
Lecture Series by
Agathoklis Giaralis, Ph.D., M.ASCE., P.E. City, University of London
Visiting Professor Sapienza University of Rome
DEEP LEARNING BASED MULTIPLE REGRESSION TO PREDICT TOTAL COLUMN WATER VAPOR (...IJDKP
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Total column water vapor is an important factor for the weather and climate. This study apply
deep learning based multiple regression to map the TCWV with elements that can improve
spatiotemporal prediction. In this study, we predict the TCWV with the use of ERA5 that is the
fifth generation ECMWF atmospheric reanalysis of the global climate. We use an appropriate
deep learning based multiple regression algorithm using Keras library to improve nonlinear
prediction between Total Column water vapor and predictors as Mean sea level pressure, Surface
pressure, Sea surface temperature, 100 metre U wind component, 100 metre V wind component,
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33 Measurement of beam-recoil observables Ox, Oz and target asymmetry T for t...Cristian Randieri PhD
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Measurement of beam-recoil observables Ox, Oz and target asymmetry T for the reaction Ī³Ļ ā K+Ī - The European Physical Journal A, Hadrons and Nuclei, February 2009, Vol. 39, N. 2, pp. 149ā161, ISSN: 1434-6001, doi: 10.1140/epja/i2008-10713-4
di A. Lleres, O. Bartalini, V. Bellini, J. P. Bocquet, P. Calvat, M. Capogni, L. Casano, M. Castoldi, A. DāAngelo, J. P. Didelez, R. Di Salvo, A. Fantini, D. Franco, C. Gaulard, G. Gervino, F. Ghio, B. Girolami, A. Giusa, M. Guidal, E. Hourany, R. Kunne, V. Kuznetsov, A. Lapik, P. Levi Sandri, F. Mammoliti, G. Mandaglio, D. Moricciani, A. N. Mushkarenkov, V. Nedorezov, L. Nicoletti, C. Perrin, C. Randieri, D. Rebreyend, F. Renard, N. Rudnev, T. Russew, G. Russo, C. Schaerf, M. L. Sperduto, M. C. Sutera, A. Turinge, V. Vegna (2009)
Abstract
The double polarization (beam-recoil) observables Ox and Oz have been measured for the reac- tion Ī³p ā K+Ī from threshold production to E ā¼ 1500MeV. The data were obtained with the linearly polarized beam of the GRAAL facility. Values for the target asymmetry T could also be extracted despite the use of an unpolarized target. Analyses of our results by two isobar models tend to confirm the necessity to include new or poorly known resonances in the 1900MeV mass region.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
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The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3ķ95 ā¤ ķķķøķ. This method can provide an
algorithm reference for the design of automatic calibration equipment.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
Ā
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3ķ95 ā¤ ķķķøķ. This method can provide an
algorithm reference for the design of automatic calibration equipment.
A Non Parametric Estimation Based Underwater Target ClassifierCSCJournals
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Underwater noise sources constitute a prominent class of input signal in most underwater signal processing systems. The problem of identification of noise sources in the ocean is of great importance because of its numerous practical applications. In this paper, a methodology is presented for the detection and identification of underwater targets and noise sources based on non parametric indicators. The proposed system utilizes Cepstral coefficient analysis and the Kruskal-Wallis H statistic along with other statistical indicators like F-test statistic for the effective detection and classification of noise sources in the ocean. Simulation results for typical underwater noise data and the set of identified underwater targets are also presented in this paper.
Diagnosis of Faulty Sensors in Antenna Array using Hybrid Differential Evolut...IJECEIAES
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method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3ķ95 ā¤ ķķķøķ. This method can provide an
algorithm reference for the design of automatic calibration equipment.
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The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3š95 ā¤ šššøš. This method can provide an
algorithm reference for the design of automatic calibration equipment.
Evolutionary generation and degeneration of randomness to assess the independ...David F. Barrero
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Randomness tests are a key tool to assess the quality of pseudo-random and true random (physical) number generators. They exploit some properties of random numbers to quantify to which extent the observed behavior of the tested sequence approximates the expected one. Given the many sides of randomness, there is not an unique test providing the whole picture, it is needed instead a suite of tests assessing different aspects randomness. A robust test suite must include independent tests, otherwise tests would assess the same property, providing redundant information. This paper addresses the independence assessment of a popular test suite named Ent. To this end we generate a large number of pseudo-random numbers with different degrees of randomness by evolving them with a Genetic Algorithm. The numbers are generated to maximize their diversity attending different criteria based on Ent output, used as fitness. We encourage diversity by maximizing and minimizing randomness measures. Once a diverse set of pseudo-random numbers is generated, the Ent test suite is run on them, and their statistics studied by means of a classical correlation analysis. The results show high correlation among some statistics used in the literature, which could be overestimating the quality of their randomness source.
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Data Assimilation for the Lorenz (1963) Model using Ensemble and Extended Kalman Filter
1. Biogeodynamics and Earth System
Sciences Summer School (BESS)Sciences Summer School (BESS)
Data Assimilation for the Lorenz
(1963) Model using Ensemble
d E t d d K l Filtand Extended Kalman Filter
D. Pasetto (a) and C. Vitolo (b)
Advisor M.Ghil, ENS & UCLA
(a) Universita' di Padova (Italy)
(b) Imperial College London (UK)
2. Data Assimilation for the Lorenz model using
Ensemble and Extended Kalman FilterEnsemble and Extended Kalman Filter
OUTLINE
Data AssimilationData Assimilation
Kalman Filter (EnKF and EKF)
Lorenz ModelLorenz Model
Results of Sensitivity Tests
Future Challenges
3. DATA ASSIMILATIONSS O
Data Assimilation is usually defined as
āEstimation and prediction (analysis) of an unknown trueEstimation and prediction (analysis) of an unknown true
state by combining observations and system dynamics
(model output)ā( p )
It is needed in order to:
- Reduce uncertainties and biases
- Improve forecasting
- Estimate initial state of a system (e g hydrologic system)Estimate initial state of a system (e.g. hydrologic system)
from multiple sources of information
- Permit forecast adjustmentsPermit forecast adjustments
4. EXAMPLE
Lorenz's Model
a simplified model of thermal convectiona simplified model of thermal convection
in the atmosphere.
Outcome:
- No predictable
pattern
- Butterfly effect
5. A BIT OF MATHS...O S
The Lorenz model
Bistability and
chaotic behaviour
Where:
For the bistable behaviour:Matlab code to simulate For the bistable behaviour:
ļ¢ = 8/3, ļ² =1.01, ļ³ = 10
For the Lorenz attractor:
Matlab code to simulate
the model dynamics
Perturbation of a ātrue runā
ļ¢ = 8/3, ļ² =28, ļ³ = 10
Perturbation of a true run
with a random noise to get
āpseudo-observationsāp
6. A BIT MORE MATHSO S
Kalman FilterKalman Filter
Data assimilation in non linear models: EKF, EnKF and Particle Filters
13. CONCLUSIONSCO C US O S
Data Assimilation on the Lorenz Model using EnKF
and EKF was performed:and EKF was performed:
Sensitivity tests on the number of realization on the
EnKF show that N=10 is already optimal for this
small model
Sensitivity tests on the observation time steps
sho the error increases as the amo nt ofshow the error increases as the amount of
information provided decreases
EnKF and EKF perform similarly but EKF is to be
prefered because computationally more efficientprefered because computationally more efficient
14. FUTURE CHALLENGESU U C G S
Further sensitivity tests
(e.g. ābistable dynamicsā)
Parameter estimationParameter estimation
Utilize Data Assimilation for āreal
problemsā (e.g. Weather predictions)
15. Data Assimilation for the Lorenz model using
Ensemble and Extended Kalman FilterEnsemble and Extended Kalman Filter
Thanks for your attentionThanks for your attention