scholarly journals A Spring–Dashpot System for Modelling Lung Tumour Motion in Radiotherapy

2010 ◽  
Vol 11 (1) ◽  
pp. 13-26 ◽  
Author(s):  
P. L. Wilson ◽  
J. Meyer
Keyword(s):  
Numerical Scheme ◽  
3D Model ◽  
Tracking System ◽  
Spatial Relationship ◽  
Single Equation ◽  
Lung Tumour ◽  
Model Parameters ◽  
Optimization Routine ◽  
Parameter Ranges ◽  
Differential Equations Models

A 3D system of springs and dashpots is presented to model the motion of a lung tumour during respiration. The main guiding factor in configuring the system is the spatial relationship between abdominal and lung tumour motion. A coupled, non-dimensional triple of ordinary differential equations models the tumour motion when driven by a 3D breathing signal. Asymptotic analysis is used to reduce the system to a single equation driven by a 3D signal, in the limit of small lateral and transverse tumour motions. A numerical scheme is introduced to solve this equation, and tested over wide parameter ranges. Real clinical data is used as input to the model, and the tumour motion output is in excellent agreement with that obtained by a prototype tumour tracking system, with model parameters obtained by optimization. The fully 3D model has the potential to accurately model the motion of any lung tumour given an abdominal signal as input, with model parameters obtained from an internal optimization routine.

PO-1739 Optimal threshold of model parameters for the respiratory tracking system with helical tomotherapy

2021 ◽  
Vol 161 ◽  
pp. S1461-S1462
Author(s):  
W. Okada ◽  
M. Tanooka ◽  
H. Doi ◽  
K. Sano ◽  
M. Shibata ◽  
...  
Keyword(s):  
Helical Tomotherapy ◽  
Tracking System ◽  
Optimal Threshold ◽  
Model Parameters ◽  
Respiratory Tracking

scholarly journals Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5549
Author(s):  
Ossi Kaltiokallio ◽  
Roland Hostettler ◽  
Hüseyin Yiğitler ◽  
Mikko Valkama
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Tracking System ◽  
Model Parameters ◽  
Calibration Data ◽  
Tracking Accuracy ◽  
Time Period ◽  
The Em Algorithm ◽  
Device Free ◽  
Localization And Tracking

Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was not occupied by people, or measurements while a person stands in known locations. Such calibration periods can be very expensive in terms of time and effort, making system deployment and maintenance challenging. This paper develops an Expectation-Maximization (EM) algorithm based on Gaussian smoothing for estimating the unknown RSS model parameters, liberating the system from supervised training and calibration periods. To fully use the EM algorithm’s potential, a novel localization-and-tracking system is presented to estimate a target’s arbitrary trajectory. To demonstrate the effectiveness of the proposed approach, it is shown that: (i) the system requires no calibration period; (ii) the EM algorithm improves the accuracy of existing DFLT methods; (iii) it is computationally very efficient; and (iv) the system outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy.

Dynamic model based formation control and obstacle avoidance of multi-robot systems

Robotica ◽  
2008 ◽  
Vol 26 (3) ◽  
pp. 345-356 ◽  
Author(s):  
Celso De La Cruz ◽  
Ricardo Carelli
Keyword(s):  
Dynamic Model ◽  
Obstacle Avoidance ◽  
Formation Control ◽  
Inverse Dynamics ◽  
Single Equation ◽  
Model Parameters ◽  
Robot System ◽  
System A ◽  
Robot Systems ◽  
Multi Robot

SUMMARYThis work presents, first, a complete dynamic model of a unicycle-like mobile robot that takes part in a multi-robot formation. A linear parameterization of this model is performed in order to identify the model parameters. Then, the robot model is input-output feedback linearized. On a second stage, for the multi-robot system, a model is obtained by arranging into a single equation all the feedback linearized robot models. This multi-robot model is expressed in terms of formation states by applying a coordinate transformation. The inverse dynamics technique is then applied to design a formation control. The controller can be applied both to positioning and to tracking desired robot formations. The formation control can be centralized or decentralized and scalable to any number of robots. A strategy for rigid formation obstacle avoidance is also proposed. Experimental results validate the control system design.

A Sensitivity Study of the Porcine Head Subjected to Bump Impact

2016 ◽  
Author(s):  
Kimberly A. Thompson ◽  
Adam C. Sokolow ◽  
Juliana Ivancik ◽  
Timothy G. Zhang ◽  
William H. Mermagen ◽  
...  
Keyword(s):  
Human Brain ◽  
3D Model ◽  
Porcine Model ◽  
Load Transfer ◽  
Numerical Study ◽  
Sensitivity Study ◽  
Complex Problem ◽  
Model Parameters ◽  
Skin Thickness ◽  
The Impact

Understanding load transfer to the human brain is a complex problem that has been a key subject of recent investigations [4–6]. Because the porcine is a gyrencephalic species, having greater structural and functional similarities to the human brain than other lower species outlined in the literature, it is commonly chosen as a surrogate for human brain studies [7]. Consequently, we have chosen to use a porcine model in this work. To understand stress wave transfer to and through the brain, it is important to fully characterize the nature of the impact (i.e. source, location, and speed) as well as the response of the constituent tissues under such impact. We suspect the material and topology of these tissues play an important role in their response. In this paper, we report on a numerical study assessing the sensitivity of model parameters for a 6-month old Gottingen mini-pig model, under bump loading. In this study, 2D models are used for computational simplicity. While a 3D model is more realistic in nature, a 2D representation is still valuable in that it can provide trends on parameter sensitivity that can help steer the development of the 3D model. In this work, we investigate the variation of skull and skin thickness, evaluate material variability of the skull, and consider the effects of nasal cavities on load transfer. Eighty simulations are computed in LS-DYNA and analyzed in MATLAB. The results of this study will provide useful knowledge on the necessary components and parameters of the porcine model and therefore provide more confidence in the analysis. This is an essential first step as we look toward bridging the gap between correlates of injury in animal and human models.

DYPIC - Dynamic Positioning in Ice: First Phase of Model Testing

2012 ◽  
Author(s):  
Andrea Haase ◽  
Solange van der Werff ◽  
Peter Jochmann
Keyword(s):  
Tracking System ◽  
Development Project ◽  
Model Testing ◽  
Fine Tuning ◽  
Model Parameters ◽  
Second Phase ◽  
Dynamic Positioning ◽  
The European Union ◽  
Testing Procedures ◽  
Ice Conditions

DYPIC (Dynamic Positioning in Ice) is a research and development project within the MARTEC ERA-NET project of the European Union. Its objective is to contribute to the closure of the gap between DP in open water being an industry standard, and DP in ice which has some extra challenges to tackle. Two phases of model testing in ice form the back bone of the project and are facilitated by HSVA (Hamburg Ship Model Basin, Germany). The first test phase, which was executed from May to July 2011, involved two different model ships. Both were tested in free floating mode (where the model sailed solely by its own propulsion system) and fixed mode (where the model was connected to a carriage). In the free floating mode the controlling was performed by a prototype DP system scaled to model parameters. Four different managed ice fields with systematically varied ice concentration and ice floe size were prepared in the ice tank in order to investigate the influence of the relevant parameters. Tests were executed for several velocities and headings with respect to the approaching ice floes. In the free floating case ice loads on the hull were derived from the measured loads on the thrusters. The behavior of the model ship was captured by the position and heading tracking system Qualisys and several installed video cameras. The fixed mode tests serve well as a reference measurement. The results will be used to develop a model scale DP system for ice that is adjustable to different kinds of vessels and ice conditions and eventually to develop testing procedures for the assessment of the DP performance of a vessel in managed ice. A second phase of model testing for fine tuning and benchmarking the developed system will be carried out in August 2012. Within the scope of the paper is the description of the performed tests speaking of test setup and ice conditions. Analyses of results are not covered.

METHDOS OF EFFECTIVE DESIGN FOR CREATING OF PARAMETRIC 3D-MODELS OF PRODUCTS USING THE INTERNAL CAPABILITIES OF CAD-PROGRAMS

2020 ◽  
Author(s):  
E. A. Petrakova
Keyword(s):  
Programming Languages ◽  
3D Model ◽  
Three Dimensional ◽  
3D Models ◽  
Parametric Modeling ◽  
Effective Control ◽  
Model Parameters ◽  
Internal Capabilities ◽  
Program Components ◽  
Effective Design

It is known that the development of a three-dimensional parametric model is a creative process, since the same 3D-model can be built in various ways. In the article the methods for effective design of parametric 3D-models with the help of internal capabilities of CAD-program without the use of programming languages (macros) is developed. Using the methods and recommendations discussed in the article on the example of Autodesk Inventor functionality will allow the engineer to design parametric three-dimensional products in CAD-programs in the most rational way, reducing the number of errors. Recommendations for effective control of 3D-model parameters during creating of Assembly parts and 2D-drawings are given. Using the functionality discussed in the article will be useful for engineers using parametric modeling methods to create typical products, optimization and analysis of structures, development of their own database of standard products that are not in the library of CAD-program components.

REAL-TIME LUNG TUMOUR MOTION MODELING FOR ADAPTIVE RADIATION THERAPY USING LEGO MINDSTORMS

2015 ◽  
Vol 15 (02) ◽  
pp. 1540019 ◽  
Author(s):  
G. GUIDI ◽  
N. MAFFEI ◽  
A. CIARMATORI ◽  
M. G. MISTRETTA ◽  
G. GOTTARDI ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Adaptive Radiation ◽  
Tracking System ◽  
Center Of Mass ◽  
Model Performance ◽  
Abnormal Behavior ◽  
Lung Tumour ◽  
Adaptive Radiation Therapy ◽  
Motion Modeling ◽  
Lego Mindstorms

An anthropomorphic phantom was built using LEGO Mindstorms® and programmed in LabVIEW® for Adaptive Radiation Therapy (ART) purpose, to simulate the processes of breathing in the lung district during treatments. A thoracic cavity is prototyped by means of an 8 ribs apparatus and 2 artificial tumor masses, driven by intelligent brick LINUX® OS CPU. An optical surface tracking system (VisionRT®) and a QUASAR™ phantom allow correlation between physiological, robotic motion and surrogated signal. Patient's breathing phases are acquired instantaneously by InfraRed/UltraSound sensors. Through 4DCT images, tumor center of mass are individuated and tracked during respiration, to link internal–external organs motion. To quantify the degree of divergences due to dynamics organs deformation, a 4D function was obtained and simulated by our phantom. Sinusoidal signals (6, 10, 12, 15 and 17 Breaths per Minute-BPM) were used for evaluating and commissioning, thereby obtained a correlation coefficient (0.90–0.94) between QUASAR and LEGO. Validated on ideal conditions, phantom was tested in clinical practice. Breaths and CT study of 12 patients were analyzed. Fitting of real breath sinograms returned a mean R value of 0.94 (0.83–0.98) with best model performance achieved in signals with respiratory frequency less than 20 BPM. By using LEGO it is possible to reproduce real patients conditions and simulate normal and even abnormal behavior during the course of therapy, allowing spatial motion estimation.

scholarly journals Hydrological model parameter dimensionality is a weak measure of prediction uncertainty

2015 ◽  
Vol 12 (4) ◽  
pp. 3945-4004 ◽  
Author(s):  
S. Pande ◽  
L. Arkesteijn ◽  
H. Savenije ◽  
L. A. Bastidas
Keyword(s):  
Model Selection ◽  
Hydrological Model ◽  
Complex Model ◽  
Model Complexity ◽  
Model Parameters ◽  
Model Structure ◽  
Soil Moisture Accounting ◽  
Prediction Uncertainty ◽  
Parameter Ranges ◽  
Prediction Problems

Abstract. This paper shows that instability of hydrological system representation in response to different pieces of information and associated prediction uncertainty is a function of model complexity. After demonstrating the connection between unstable model representation and model complexity, complexity is analyzed in a step by step manner. This is done measuring differences between simulations of a model under different realizations of input forcings. Algorithms are then suggested to estimate model complexity. Model complexities of the two model structures, SAC-SMA (Sacramento Soil Moisture Accounting) and its simplified version SIXPAR (Six Parameter Model), are computed on resampled input data sets from basins that span across the continental US. The model complexities for SIXPAR are estimated for various parameter ranges. It is shown that complexity of SIXPAR increases with lower storage capacity and/or higher recession coefficients. Thus it is argued that a conceptually simple model structure, such as SIXPAR, can be more complex than an intuitively more complex model structure, such as SAC-SMA for certain parameter ranges. We therefore contend that magnitudes of feasible model parameters influence the complexity of the model selection problem just as parameter dimensionality (number of parameters) does and that parameter dimensionality is an incomplete indicator of stability of hydrological model selection and prediction problems.

scholarly journals A trading-space-for-time approach to probabilistic continuous streamflow predictions in a changing climate – accounting for changing watershed behavior

2011 ◽  
Vol 15 (11) ◽  
pp. 3591-3603 ◽  
Author(s):  
R. Singh ◽  
T. Wagener ◽  
K. van Werkhoven ◽  
M. E. Mann ◽  
R. Crane
Keyword(s):  
Spatial Relationship ◽  
Model Performance ◽  
Climate Scenario ◽  
Hydrologic Model ◽  
Climatic Conditions ◽  
Low Flow ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Watershed Model ◽  
The Impact

Abstract. Projecting how future climatic change might impact streamflow is an important challenge for hydrologic science. The common approach to solve this problem is by forcing a hydrologic model, calibrated on historical data or using a priori parameter estimates, with future scenarios of precipitation and temperature. However, several recent studies suggest that the climatic regime of the calibration period is reflected in the resulting parameter estimates and model performance can be negatively impacted if the climate for which projections are made is significantly different from that during calibration. So how can we calibrate a hydrologic model for historically unobserved climatic conditions? To address this issue, we propose a new trading-space-for-time framework that utilizes the similarity between the predictions under change (PUC) and predictions in ungauged basins (PUB) problems. In this new framework we first regionalize climate dependent streamflow characteristics using 394 US watersheds. We then assume that this spatial relationship between climate and streamflow characteristics is similar to the one we would observe between climate and streamflow over long time periods at a single location. This assumption is what we refer to as trading-space-for-time. Therefore, we change the limits for extrapolation to future climatic situations from the restricted locally observed historical variability to the variability observed across all watersheds used to derive the regression relationships. A typical watershed model is subsequently calibrated (conditioned) on the predicted signatures for any future climate scenario to account for the impact of climate on model parameters within a Bayesian framework. As a result, we can obtain ensemble predictions of continuous streamflow at both gauged and ungauged locations. The new method is tested in five US watersheds located in historically different climates using synthetic climate scenarios generated by increasing mean temperature by up to 8 °C and changing mean precipitation by −30% to +40% from their historical values. Depending on the aridity of the watershed, streamflow projections using adjusted parameters became significantly different from those using historically calibrated parameters if precipitation change exceeded −10% or +20%. In general, the trading-space-for-time approach resulted in a stronger watershed response to climate change for both high and low flow conditions.

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