scholarly journals Efficient Forced Response Computations of Acoustical Systems with a State-Space Approach

Acoustics ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 581-594
Author(s):  
Art J. R. Pelling ◽  
Ennes Sarradj
Keyword(s):  
State Space ◽  
Large Scale ◽  
State Space Model ◽  
Measurement Data ◽  
Numerical Linear Algebra ◽  
Forced Response ◽  
State Space Models ◽  
High Dimensional ◽  
Diagonal Form ◽  
The Past

State-space models have been successfully employed for model order reduction and control purposes in acoustics in the past. However, due to the cubic complexity of the singular value decomposition, which makes up the core of many subspace system identification (SSID) methods, the construction of large scale state-space models from high-dimensional measurement data has been problematic in the past. Recent advances of numerical linear algebra have brought forth computationally efficient randomized rank-revealing matrix factorizations and it has been shown that these factorizations can be used to enhance SSID methods such as the Eigensystem Realization Algorithm (ERA). In this paper, we demonstrate the applicability of the so-called generalized ERA to acoustical systems and high-dimensional input data by means of an example. Furthermore, we introduce a new efficient method of forced response computation that relies on a state-space model in quasi-diagonal form. Numerical experiments reveal that our proposed method is more efficient than previous state-space methods and can even outperform frequency domain convolutions in certain scenarios.

scholarly journals LSTM-Guided Coaching Assistant for Table Tennis Practice

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4112 ◽  
Author(s):  
Se-Min Lim ◽  
Hyeong-Cheol Oh ◽  
Jaein Kim ◽  
Juwon Lee ◽  
Jooyoung Park
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Series Data ◽  
State Space Model ◽  
Skill Assessment ◽  
Series Data ◽  
High Dimensional ◽  
Table Tennis ◽  
Space Model ◽  
Low Dimensional

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

scholarly journals Evaluation of methods for spawner–recruit analysis in mixed-stock Pacific salmon fisheries

2020 ◽  
Vol 77 (7) ◽  
pp. 1149-1162 ◽  
Author(s):  
Benjamin A. Staton ◽  
Matthew J. Catalano ◽  
Brendan M. Connors ◽  
Lewis G. Coggins ◽  
Michael L. Jones ◽  
...  
Keyword(s):  
State Space ◽  
Oncorhynchus Tshawytscha ◽  
Pacific Salmon ◽  
State Space Model ◽  
Reference Points ◽  
State Space Models ◽  
Space Model ◽  
Trade Offs ◽  
Salmon Fisheries ◽  
Mixed Stock

Salmon populations harvested in mixed-stock fisheries can exhibit genotypic, behavioral, and life history diversity that can lead to heterogeneity in population productivity and size. Methods to quantify this heterogeneity among populations in mixed-stock fisheries are not well-established but are critical to assessing harvest–biodiversity trade-offs when setting harvest policies. We developed an integrated, age-structured, state-space model that allows for more complete use of available data and sharing of information than simpler methods. We compared a suite of state-space models of varying structural complexity to simpler regression-based approaches and, as an example case, fitted them to data from 13 Chinook salmon (Oncorhynchus tshawytscha) populations in the Kuskokwim drainage in western Alaska. We found biological and policy conclusions were largely consistent among state-space models but differed strongly from regression-based approaches. Simulation trials illustrated our state-space models were largely unbiased with respect to spawner–recruit parameters, abundance states, and derived biological reference points, whereas the regression-based approaches showed substantial bias. These findings suggest our state-space model shows promise for informing harvest policy evaluations of harvest–biodiversity trade-offs in mixed-stock salmon fisheries.

IDENTIFICATION OF ON-AND OFF-LINE LINEAR STATE SPACE MODELS USING SUBSPACE METHODS

2015 ◽  
Vol 77 (28) ◽  
Author(s):  
Nurul Syahirah Khalid ◽  
Norhaliza Abd. Wahab ◽  
Muhammad Iqbal Zakaria
Keyword(s):  
State Space ◽  
State Space Model ◽  
Model Identification ◽  
State Space Models ◽  
Subspace Methods ◽  
State Space System ◽  
Linear State ◽  
Value Decomposition ◽  
Error State

In this paper, subspace identification methods are proposed to analyze the differences between On-And Off-Line Linear State Space Models Using Subspace Methods. There are several ways that can estimate the order of the system. For this paper, Singular Value Decomposition (SVD) is used to estimate the order of the system. Comparing with the others methods, this method only need a limited number of input and output data for the determination of the system matrices. Two methods of the subspace algorithm are used which is N4SID (Numerical algorithm for Subspace State Space System Identification) and MOESP (Multivariable Output-Error State-Space model identification).

scholarly journals A review of Bayesian state-space modelling of capture–recapture–recovery data

2012 ◽  
Vol 2 (2) ◽  
pp. 190-204 ◽  
Author(s):  
Ruth King
Keyword(s):  
State Space ◽  
State Space Model ◽  
Model Fitting ◽  
The State ◽  
State Space Models ◽  
Observation Error ◽  
Space Model ◽  
System Process ◽  
Observation Process ◽  
Capture Recapture

Traditionally, state-space models are fitted to data where there is uncertainty in the observation or measurement of the system. State-space models are partitioned into an underlying system process describing the transitions of the true states of the system over time and the observation process linking the observations of the system to the true states. Open population capture–recapture–recovery data can be modelled in this framework by regarding the system process as the state of each individual observed within the study in terms of being alive or dead, and the observation process the recapture and/or recovery process. The traditional observation error of a state-space model is incorporated via the recapture/recovery probabilities being less than unity. The models can be fitted using a Bayesian data augmentation approach and in standard BUGS packages. Applying this state-space framework to such data permits additional complexities including individual heterogeneity to be fitted to the data at very little additional programming effort. We consider the efficiency of the state-space model fitting approach by considering a random effects model for capture–recapture data relating to dippers and compare different Bayesian model-fitting algorithms within WinBUGS.

scholarly journals Extended Ensemble Filter for High-dimensional Nonlinear State Space Models

2021 ◽  
pp. 84-97
Author(s):  
Oryiema Robert ◽  
David Angwenyi ◽  
Kevin Midenyo
Keyword(s):  
State Space ◽  
Negative Impact ◽  
High Dimensional Data ◽  
Innovation Process ◽  
State Space Models ◽  
High Dimensional ◽  
First Order ◽  
Filter Performance ◽  
Noisy Observation ◽  
Nonlinear State Space Models

There are several functional forms for non-linear dynamical filters. Extended Kalman filters are algorithms that are used to estimate more accurate values of unknown quantities of internal dynamical systems from a sequence of noisy observation measured over a period of time. This filtering process becomes computationally expensive when subjected to high dimensional data which consequently has a negative impact on the filter performance in real time. This is because integration of the equation of evolution of covariances is extremely costly, especially when the dimension of the problem is huge which is the case in numerical weather prediction.This study has developed a new filter, the First order Extended Ensemble Filter (FoEEF), with a new extended innovation process to improve on the measurement and be able to estimate the state value of high dimensional data. We propose to estimate the covariances empirically, which lends the filter amenable to large dimensional models. The new filter is derived from stochastic state-space models and its performance is tested using Lorenz 63 system of ordinary differential equations and Matlab software.The performance of the newly developed filter is then compared with the performances of three other filters, that is, Bootstrap particle Filter (BPF), First order Extended Kalman Bucy Filter (FoEKBF) and Second order Extended Kalman Bucy Filter (SoEKBF).The performance of the FoEEF improves with the increase in ensemble size. Even with as low number of ensembles as 40, the FoEEF performs as good as the FoEKBF and SoEKBF. This shows, that the proposed filter can register a good performance when used in high-dimensional state-space models.

scholarly journals UFIR Filtering for GPS-Based Tracking over WSNs with Delayed and Missing Data

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Karen Uribe-Murcia ◽  
Yuriy S. Shmaliy ◽  
Jose A. Andrade-Lucio
Keyword(s):  
Missing Data ◽  
State Space ◽  
Global Positioning System ◽  
Finite Impulse Response ◽  
Smart Cities ◽  
State Space Model ◽  
Measurement Data ◽  
Vehicle Tracking ◽  
Global Positioning ◽  
Central Station

In smart cities, vehicles tracking is organized to increase safety by localizing cars using the Global Positioning System (GPS). The GPS-based system provides accurate tracking but is also required to be reliable and robust. As a main estimator, we propose using the unbiased finite impulse response (UFIR) filter, which meets these needs as being more robust than the Kalman filter (KF). The UFIR filter is developed for vehicle tracking in discrete-time state-space over wireless sensor networks (WSNs) with time-stamped data discretely delayed on k-step-lags and missing data. The state-space model is represented in a way such that the UFIR filter, KF, and H∞ filter can be used universally. Applications are given for measurement data, which are cooperatively transferred from a vehicle to a central station through several nodes with k-step-lags. Better tracking performance of the UFIR filter is shown experimentally.

Responses comparison of the two discrete-time linear fractional state-space models

2016 ◽  
Vol 19 (4) ◽  
Author(s):  
Tadeusz Kaczorek ◽  
Piotr Ostalczyk
Keyword(s):  
State Space ◽  
State Space Model ◽  
State Space Models ◽  
Discrete State ◽  
Numerical Examples ◽  
Space Model ◽  
The Difference ◽  
Time Linear ◽  
Discrete State Space ◽  
Fundamental Matrices

AbstractIn this survey we consider two fractional-order discrete state-space models of linear systems. In both cases the crucial elements are the fundamental matrices. The difference between them is analyzed. A fundamental condition for the first state-space model is given. The investigations are illustrated by the numerical examples.

Sign in / Sign up

Export Citation Format

Share Document