The aim of this study was to determine if heart rate responses to training intensity during road cycling could be modelled with compact time-variant mathematical model structures. The model performance was evaluated i...
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The aim of this study was to determine if heart rate responses to training intensity during road cycling could be modelled with compact time-variant mathematical model structures. The model performance was evaluated in terms of model order (complexity), number of inputs and parameter estimation methods used (time-invariant vs. time-variant). Thirteen male cyclists performed two identical cycling tests of 27 km on the road. Uphill sections were introduced to induce dynamic variations in heart rate. The heart rate and training intensity, represented by power output and road inclination, were measured in real-time. Taking only power as system input allowed to explain the variations in heart rate in an accurate way, since adding the road inclination as an additional input did not significantly improve the modelling performance. Furthermore, we demonstrated that models with first-order dynamics accurately describes the heart rate responses to power variations were significantly better than the first-order model structures (P=0.028). Finally, the heart rate dynamics appeared to be time-variant, since the time-variant model structures. So, compact time-variant second-order model structures could be used to model the heart rate response to training intensity as a basis for training optimisation.
In order to predict the impact of pollution incidents on rivers, it is necessary to predict the dispersion coefficient and the flow velocity corresponding to the discharge in the river of interest. This paper explores...
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In order to predict the impact of pollution incidents on rivers, it is necessary to predict the dispersion coefficient and the flow velocity corresponding to the discharge in the river of interest. This paper explores methods for doing this, particularly with a view to applications on ungauged rivers, i.e. those for which little hydraulic or morphometric data are available. An approach based on neural networks, trained on a wide-ranging database of optimized parameter values from tracer experiments and corresponding physical variables assembled for American and European rivers, is proposed. Tests using independent cases showed that the neural networks generally gave more reliable parameter estimates than a second-order polynomial regression approach. The quality of predictions of temporal concentration profiles was heavily influenced by the accuracy of the velocity prediction.
Genetic Programming is able to systematically explore many alternative model structures of different complexity from available input and response data. We hypothesised that Genetic Programming can be used to test the ...
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Genetic Programming is able to systematically explore many alternative model structures of different complexity from available input and response data. We hypothesised that Genetic Programming can be used to test the structure of hydrological models and to identify dominant processes in hydrological systems. To test this, Genetic Programming was used to analyse a data set from a lysimeter experiment in southeastern Australia. The lysimeter experiment was conducted to quantify the deep percolation response under surface irrigated pasture to different soil types, watertable depths and water ponding times during surface irrigation. Using Genetic Programming, a simple model of deep percolation was recurrently evolved in multiple Genetic Programming runs. This simple and interpretable model supported the dominant process contributing to deep percolation represented in a conceptual model that was published earlier. Thus, this study shows that Genetic Programming can be used to evaluate the structure of hydrological models and to gain insight about the dominant processes in hydrological systems. (C) 2010 Elsevier B.V. All rights reserved.
This research aims at quantifying transient spatial gradients in sweat production on a human head while cycling. Six test persons were studied. Each test lasted 30 min while a change in work rate was applied after 5 m...
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This research aims at quantifying transient spatial gradients in sweat production on a human head while cycling. Six test persons were studied. Each test lasted 30 min while a change in work rate was applied after 5 min (from 80 to 150 W for males and from 50 to 125 W for females). Two conditions were analyzed in this research: warm (28.3 +/- 0.1 degrees C, 38 +/- 0.6% RH and 0.1 +/- 0.1 m/s air velocity) and standard (16.1 degrees +/- 0.2 degrees C, 45% +/- 0.6 RH and 2.4 +/- 0.2 m/s air velocity). Sweat production of the head was measured as a function of time on the right temple, left temple and forehead. This allowed modelling the dynamics of the sweat production response. Constant steady state sweat production, time delay in sweat production, time constant of sweat production and steady state gain of sweat production were quantified and analyzed. Time constants of sweat production were shorter in the warm condition compared to the standard condition. Mean and SEM time constant of sweat production varied from 561 +/- 144 s (frontal region) to 1117 +/- 230s (left temple) and 1080 +/- 232 s (right temple) in the warm condition. While, at the standard condition, the time constant of sweat production varied from 873 +/- 121 s at the frontal region to 1431 +/- 195 s at the left temple and 1727 +/- 196 s at the right temple. Additionally, also constant steady state sweat production was 0.4-0.7 mg min(-1) cm(-2) higher in the warm compared to the standard condition (P<0.05). However, no differences (P>0.05) were observed for steady state gain and time delay of sweat production between the standard and warm condition. The results of this research can be used to enhance physiological insight of the sweating process and it can also help to develop sweating thermal manikins that behave more realistically to thermal changes. Knowledge of sweat production might also be valuable when designing active controlled headgear since the reaction time of the actuator should take the
In this paper, a fault tolerant control (FTC) for a dearomatisation process in the presence of faults in online product quality analysers is presented. The FTC consists of a fault detection system (FDI) and a logic fo...
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In this paper, a fault tolerant control (FTC) for a dearomatisation process in the presence of faults in online product quality analysers is presented. The FTC consists of a fault detection system (FDI) and a logic for triggering predefined FTC actions. FDI is achieved by combining several process data driven approaches for detecting faults in online quality analysers. The FTC exploits the diagnostic information in adapting a quality controller (MPC) to the faulty situation by manipulating tuning parameters of the MPC to produce both proactive and reactive strategies. The proposed FTC was implemented, tested offline and validated onsite at the Naantali oil refinery. The successful testing and plant validation results are presented and discussed. (C) 2009 Published by Elsevier Ltd.
This paper presents the result of an investigation into the Proportional Integral Plus (PIP) control of nitrate in the second zone of an activated sludge benchmark. A data-based reduced order model is used as the cont...
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This paper presents the result of an investigation into the Proportional Integral Plus (PIP) control of nitrate in the second zone of an activated sludge benchmark. A data-based reduced order model is used as the control model and identified using the Simplified Refined Instrumental Variable (SRIV) identification and estimation algorithm. The PIP control design is based on the Non Minimum State Space (NMSS) form and State Variable Feedback (SVF) methodology. The PIP controller is tested against dynamic load disturbances and compared with the response of a well tuned PI controller.
This paper discusses the data-based Mechanistic (DBM) approach to modelling the micro-climate in agricultural buildings. Here, the imperfect mixing processes that dominate the system behaviour during forced ventilatio...
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This paper discusses the data-based Mechanistic (DBM) approach to modelling the micro-climate in agricultural buildings. Here, the imperfect mixing processes that dominate the system behaviour during forced ventilation are first modelled objectively, in purely data-based terms, by continuous-time transfer function relationships. In their equivalent differential equation form, however, these models can be interpreted in terms of the Active Mixing Volume (AMV) concept, developed previously at Lancaster in connection with pollution transport in rivers and soils and, latterly, in modelling the micro-climate of horticultural glasshouses. This can be compared with the incomplete mixing and control volume concepts that have been investigated previously at Leuven. The data used in the initial stages of the research project, as described in the paper, have been obtained from a series of planned ventilation experiments carried out in a large instrumented chamber at Leuven. The overall objectives of this collaborative study are twofold: first, to pain a better understanding of the heat transfer and micro-climate dynamics in the chamber;and second, to develop models that can form the basis for the design of optimal Proportional-Integral-Plus (PIP-LQ) climate control systems for livestock buildings of a kind used previously for controlling the micro-climate in horticultural glasshouses. Although not specifically directed at glasshouse systems, the techniques described in the paper can be applied straightforwardly within a glasshouse context. (C) 2000 Elsevier Science B.V. All rights reserved.
This paper discusses the data-based Mechanistic (DBM) approach to modelling the micro-climate in agricultural buildings. Here, the imperfect mixing processes that dominate the system behaviour during forced ventilatio...
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This paper discusses the data-based Mechanistic (DBM) approach to modelling the micro-climate in agricultural buildings. Here, the imperfect mixing processes that dominate the system behaviour during forced ventilation are first modelled objectively, in purely data-based terms, by continuous-time transfer function relationships. In their equivalent differential equation fonn, however, these models can be interpreted in terms of the Active Mixing Volume (AMV) concept, developed previously at Lancaster in connection with pollution transport in rivers and soils and, latterly, in modelling the microclimate of horticultural glasshouses. This can be compared with the incomplete mixing and control volume concepts that have been investigated previously at Leuven. The data used in the initial stages of the research project, as described in the paper, have been obtained from a series of planned ventilation experiments carried out in a large instrumented chamber at Leuven. The overall objectives of this collaborative study are two-fold: first, to gain a better understanding of the heat transfer and micro-climate dynamics in the chamber; and second, to develop models that can form the basis for the design of optimal ProportionalIntegral- Plus (PIP-LQ) climate control systems for livestock buildings of a kind used previously for controlling the micro-climate in horticultural glasshouses.
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