Existing mathematical models for predicting neurobehavioural performance are not suited for mobile computing platforms because they cannot adapt model parameters automatically in real time to reflect individual differ...
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Existing mathematical models for predicting neurobehavioural performance are not suited for mobile computing platforms because they cannot adapt model parameters automatically in real time to reflect individual differences in the effects of sleep loss. We used an extended Kalman filter to develop a computationally efficient algorithm that continually adapts the parameters of the recently developed Unified Model of Performance (UMP) to an individual. The algorithm accomplishes this in real time as new performance data for the individual become available. We assessed the algorithm's performance by simulating real-time model individualization for 18 subjects subjected to 64 h of total sleep deprivation (TSD) and 7 days of chronic sleep restriction (CSR) with 3 h of time in bed per night, using psychomotor vigilance task (PVT) data collected every 2 h during wakefulness. This UMP individualization process produced parameter estimates that progressively approached the solution produced by a post-hoc fitting of model parameters using all data. The minimum number of PVT measurements needed to individualize the model parameters depended upon the type of sleep-loss challenge, with similar to 30 required for TSD and similar to 70 for CSR. However, model individualization depended upon the overall duration of data collection, yielding increasingly accurate model parameters with greater number of days. Interestingly, reducing the PVT sampling frequency by a factor of two did not notably hamper model individualization. The proposed algorithm facilitates real-time learning of an individual's trait-like responses to sleep loss and enables the development of individualized performance prediction models for use in a mobile computing platform.
The purpose of this paper is first, to describe briefly the basic form of the CAPTAIN computer package; and second, to discuss various enhancements of this basic package which have been developed during the past few y...
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The purpose of this paper is first, to describe briefly the basic form of the CAPTAIN computer package; and second, to discuss various enhancements of this basic package which have been developed during the past few years and which will be used to evolve future CAPTAIN software for different kinds of application. These enhancements include more efficient and objective forms of model structure identification; the refinement of the basic instrumental variable estimation procedure so that the estimates are more statistically efficient; the extension of the various algorithms to handle multi-input, transfer function models as well as multivariable (multi-input, multi-output) models; the ability to estimate parameters in continuous-time transfer function models; the design of adaptive state reconstructors (estimators); and, finally, the extension to handle general stochastically variable model parameters. The paper contains the results of a number of practical examples which demonstrate these various options.
In most industrial processes the applied controller is adjusted roughly or according to experiences with the process. If the dynamics of the processes are not sufficiently known. the controller has to be re tuned manu...
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In most industrial processes the applied controller is adjusted roughly or according to experiences with the process. If the dynamics of the processes are not sufficiently known. the controller has to be re tuned manually in order to ensure a desired control behaviour. This tuning delays the operation in the stationary phase and thus wastes time and material .This paper presents a predictive control strategy for the start-up phase of processes with switching actuators which yet can be applied to systems with linear control elements as well. The parameters of the unknown process are estimated on-line during start-up without any pre-identification. The process reaches the stationary phase quickly by simultaneously avoiding undesired states of the process such as overshooting or long settling-time. The process information gathered during the start-up phase is used also to adjust a controller for the stationary operation. The results of this strategy are compared with the start-up procedures achieved with conventional onoff controllers in order to judge its performance
This paper presents an application of the self-tuning regulator to an industrial phosphate drying furnace. The purpose of the regulator is to keep the moisture content of the dried phosphate close to a certain desired...
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This paper presents an application of the self-tuning regulator to an industrial phosphate drying furnace. The purpose of the regulator is to keep the moisture content of the dried phosphate close to a certain desired value, and at the same time to minimize the fuel consumption. Several simulation experiments were carried out. A single-input single-output model of the furnace is assumed. A controller minimizing the variance of an auxiliary output is employed. Good control performance is achieved. The results demonstrate the feasibility of the presented algorithm.
This paper presents the results of collaborative research from the SUPERGEN FlexNet Consortium into Wide Area Monitoring, Protection and Control (WAMPAC). The focus of the research addresses the design and development...
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ISBN:
(纸本)9781457710001
This paper presents the results of collaborative research from the SUPERGEN FlexNet Consortium into Wide Area Monitoring, Protection and Control (WAMPAC). The focus of the research addresses the design and development of an optimal WAMPAC architecture, communication infrastructure and real-time WAMPAC applications which will play an important role in future GB power network operation and understanding. The article concludes with an assessment of inter-area oscillations based on data records captured by the wide area monitoring system (WAMS) established as part of the FlexNet project.
This paper presents a real-time algorithm for identifying the inductance and capacitance values of LCL filters used with grid converters. As a side product, the grid inductance seen from the point of common coupling i...
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This paper presents a real-time algorithm for identifying the inductance and capacitance values of LCL filters used with grid converters. As a side product, the grid inductance seen from the point of common coupling is also estimated. A wideband excitation signal is added to the converter voltage reference. During the excitation, the converter currents and the converter voltage reference are sampled. The samples are preprocessed in real time by removing DC biases and significant grid-frequency harmonics. parameters of a discrete-time model are estimated at each sampling instant with a recursiveestimation algorithm. The model parameter estimates are translated into inductance and capacitance values. The method can be embedded to a control system of PWM-based converters in a plug-in manner. Only the DC-link voltage and converter currents need to be measured. Simulation and experimental results are presented for a 12.5-kVA grid converter system to evaluate the proposed method.
作者：
Bergman, M.J.Delleur, J.W.Respectively
Engineer Albert H. Halff & Associates Inc. 8616 Northwest Plaza Drive Dallas Texas 75225 and Professor
School of Civil Engineering Purdue University West Lafayette Indiana 47907.
Results are reported from an application of the state space formulation and the Kalman filter to real‐time forecasting of daily river flows. It is shown that the application of filtering techniques improves the overa...
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作者：
Bergman, M.J.Delleur, J.W.Respectively
Engineer Albert H. Halff & Associates Inc. 8616 Northwest Plaza Drive Dallas Texas 75225 and Professor
School of Civil Engineering Purdue University West Lafayette Indiana 47907.
An important class of models, frequently used in hydrology for the forecasting of hydrologic variables one or more time periods ahead, or for the generation of synthetic data sequences, is the class of autoregressive(...
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