144,415 research outputs found
Extension of the Bergman Minimal Model for the Glucose-insulin Interaction
In this paper, the extension of the Bergman model (minimal model) is
proposed with an internal insulin control (IIC) part, representing the own
insulin control of the human body. The model has been verified with clinical
experiments, by oral glucose intake tests. Employing parameter estimation,
for inverse problem solution technique (SOSI - `single output single
input´) was developed using Chebysev shifted polynomials, and linear
identification in time domain based on measured glucose and insulin
concentration values was applied. The glucose and insulin input functions
have been approximated and the model parameters of IIC were estimated. This
extended Bergman model suits considerably better to the practical clinical
situation, and it can improve the effectivity of the external control design
for glucose-insulin process. The IIC part has been identified via dynamical
neural network using the proposed SOSI technique. The symbolic and numerical
computations were carried out with Mathematica 5.1, and with its application Neural
Networks 2.0
Fault detection, identification and accommodation techniques for unmanned airborne vehicles
Unmanned Airborne Vehicles (UAV) are assuming prominent roles in both the commercial and military aerospace industries. The promise of reduced costs and reduced risk to human life is one of their major attractions, however these low-cost systems are yet to gain acceptance as a safe alternate to manned solutions. The absence of a thinking, observing, reacting and decision making pilot reduces the UAVs capability of managing adverse situations such as faults and failures. This paper presents a review of techniques that can be used to track the system health onboard a UAV. The review is based on a year long literature review aimed at identifying approaches suitable for combating the low reliability and high attrition rates of today’s UAV. This research primarily focuses on real-time, onboard implementations for generating accurate estimations of aircraft health for fault accommodation and mission management (change of mission objectives due to deterioration in aircraft health). The major task of such systems is the process of detection, identification and accommodation of faults and failures (FDIA). A number of approaches exist, of which model-based techniques show particular promise. Model-based approaches use analytical redundancy to generate residuals for the aircraft parameters that can be used to indicate the occurrence of a fault or failure. Actions such as switching between redundant components or modifying control laws can then be taken to accommodate the fault. The paper further describes recent work in evaluating neural-network approaches to sensor failure detection and identification (SFDI). The results of simulations with a variety of sensor failures, based on a Matlab non-linear aircraft model are presented and discussed. Suggestions for improvements are made based on the limitations of this neural network approach with the aim of including a broader range of failures, while still maintaining an accurate model in the presence of these failures
Development of vision-based soft sensing techniques with training in virtual environment for autonomous vehicle control
The goal of this master thesis is to develop an original approach to lane estimation for scaled
vehicles using a front-mounted camera and convolutional neural networks. The key components of
this estimation process are the fact that all the training is performed in simulation using a
noisy path; and the online inference is performed on low-end hardware (Raspberry Pi 4) in an
efficient and responsive way, while being very accurate. The heading error of the standard pure
pursuit controller is chosen as estimation target. A clothoid based centerline has been chosen
as training path for its several advantages in the analyzed scenario. Different performance
metrics are evaluated and the standard deviation of the error is found to be the more effective. An
analysis on the hyperparameters (image dimension, lookahead distance,
training variability, and others) is performed in order to find the best combinations and
evaluate the impact of each parameter. From the results in a real world scenario a very small
network and image and a very high training variability resulted as the best overall combination,
with the network complexity and training variability playing a major role in the accuracy of the
system. The whole process is finally tested in a real life control loop achieving very good
performance, allowing for precise lane tracking using delayless local estimation.The goal of this master thesis is to develop an original approach to lane estimation for scaled
vehicles using a front-mounted camera and convolutional neural networks. The key components of
this estimation process are the fact that all the training is performed in simulation using a
noisy path; and the online inference is performed on low-end hardware (Raspberry Pi 4) in an
efficient and responsive way, while being very accurate. The heading error of the standard pure
pursuit controller is chosen as estimation target. A clothoid based centerline has been chosen
as training path for its several advantages in the analyzed scenario. Different performance
metrics are evaluated and the standard deviation of the error is found to be the more effective. An
analysis on the hyperparameters (image dimension, lookahead distance,
training variability, and others) is performed in order to find the best combinations and
evaluate the impact of each parameter. From the results in a real world scenario a very small
network and image and a very high training variability resulted as the best overall combination,
with the network complexity and training variability playing a major role in the accuracy of the
system. The whole process is finally tested in a real life control loop achieving very good
performance, allowing for precise lane tracking using delayless local estimation
State estimation for coupled uncertain stochastic networks with missing measurements and time-varying delays: The discrete-time case
Copyright [2009] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper is concerned with the problem of state estimation for a class of discrete-time coupled uncertain stochastic complex networks with missing measurements and time-varying delay. The parameter uncertainties are assumed to be norm-bounded and enter into both the network state and the network output. The stochastic Brownian motions affect not only the coupling term of the network but also the overall network dynamics. The nonlinear terms that satisfy the usual Lipschitz conditions exist in both the state and measurement equations. Through available output measurements described by a binary switching sequence that obeys a conditional probability distribution, we aim to design a state estimator to estimate the network states such that, for all admissible parameter uncertainties and time-varying delays, the dynamics of the estimation error is guaranteed to be globally exponentially stable in the mean square. By employing the Lyapunov functional method combined with the stochastic analysis approach, several delay-dependent criteria are established that ensure the existence of the desired estimator gains, and then the explicit expression of such estimator gains is characterized in terms of the solution to certain linear matrix inequalities (LMIs). Two numerical examples are exploited to illustrate the effectiveness of the proposed estimator design schemes
Connections Between Adaptive Control and Optimization in Machine Learning
This paper demonstrates many immediate connections between adaptive control
and optimization methods commonly employed in machine learning. Starting from
common output error formulations, similarities in update law modifications are
examined. Concepts in stability, performance, and learning, common to both
fields are then discussed. Building on the similarities in update laws and
common concepts, new intersections and opportunities for improved algorithm
analysis are provided. In particular, a specific problem related to higher
order learning is solved through insights obtained from these intersections.Comment: 18 page
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Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation
Satellite-based remotely sensed data have the potential to provide hydrologically relevant information about spatially and temporally varying physical variables. A methodology for estimating such variables from multichannel remotely sensed data is presented; the approach is based on a modified counterpropagation neural network (MCPN) and is both effective and efficient at building complex nonlinear input-output function mappings from large amounts of data. An application to high-resolution estimation of the spatial and temporal variation of surface rainfall using geostationary satellite infrared and visible imagery is presented. Test results also indicate that spatially and temporally sparse ground-based observations can be assimilated via an adaptive implementation of the MCPN method, thereby allowing on-line improvement of the estimates
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