Coupling model uncertainty for coupled rainfall/runoff and surface water quality models in river problems

Coupled hydrology and water quality models are an important tool today, used in the understanding and management of surface water and watershed areas. Such problems are generally subject to substantial uncertainty in parameters, process understanding, and data. Component models, drawing on different data, concepts, and structures, are affected differently by each of these uncertain elements. This paper proposes a framework wherein the response of component models to their respective uncertain elements can be quantified and assessed, using a hydrological model and water quality model as two exemplars. The resulting assessments can be used to identify model coupling strategies that permit more appropriate use and calibration of individual models, and a better overall coupled model response. One key finding was that an approximate balance of water quality and hydrological model responses can be obtained using both the QUAL2E and Mike11 water quality models. The balance point, however, does not support a particularly narrow surface response (or stringent calibration criteria) with respect to the water quality calibration data, at least in the case examined here. Additionally, it is clear from the results presented that the structural source of uncertainty is at least as significant as parameter-based uncertainties in areal models. © 2012 John Wiley & Sons, Ltd.

Improving model quality through computer controlled saturation

In dynamic centrifuge modelling, fluids with enhanced viscosity are often used to correct for the discrepancy in time scaling. However, great care must be taken when using a high viscosity fluid that hydraulic gradients during saturation do not become high enough to cause excessive model disturbance. This paper introduces the CAM-Sat system which aims to improve the saturation process by continually controlling the fluid flow into the model, limiting it to rates low enough to avoid model disturbance. A new method for measuring the fluid flow rate is then described, and its implementation & improvement to the system is discussed. © 2010 Taylor & Francis Group, London.

A bulk acoustic mode single-crystal silicon microresonator with a high-quality factor

This paper details a bulk acoustic mode resonator fabricated in single-crystal silicon with a quality factor of 15 000 in air, and over a million below 10 mTorr at a resonant frequency of 2.18 MHz. The resonator is a square plate that is excited in the square-extensional mode and has been fabricated in a commercial foundry silicon-on-insulator (SOI) MEMS process through MEMSCAP. This paper also presents a simple method of extracting resonator parameters from raw measurements heavily buried in electrical feedthrough. Its accuracy has been demonstrated through a comparison between extracted motional resistance values measured at different voltage biases and those predicted from an analytical model. Finally, a method of substantially cancelling electrical feedthrough through system-level electronic implementation is also introduced. © 2008 IOP Publishing Ltd.

Effects of the user model on simulation-based learning of dialogue strategies

Over the past decade, a variety of user models have been proposed for user simulation-based reinforcement-learning of dialogue strategies. However, the strategies learned with these models are rarely evaluated in actual user trials and it remains unclear how the choice of user model affects the quality of the learned strategy. In particular, the degree to which strategies learned with a user model generalise to real user populations has not be investigated. This paper presents a series of experiments that qualitatively and quantitatively examine the effect of the user model on the learned strategy. Our results show that the performance and characteristics of the strategy are in fact highly dependent on the user model. Furthermore, a policy trained with a poor user model may appear to perform well when tested with the same model, but fail when tested with a more sophisticated user model. This raises significant doubts about the current practice of learning and evaluating strategies with the same user model. The paper further investigates a new technique for testing and comparing strategies directly on real human-machine dialogues, thereby avoiding any evaluation bias introduced by the user model. © 2005 IEEE.

A model of driver steering control incorporating the driver's sensing of steering torque

Steering feel, or steering torque feedback, is widely regarded as an important aspect of the handling quality of a vehicle. Despite this, there is little theoretical understanding of its role. This paper describes an initial attempt to model the role of steering torque feedback arising from lateral tyre forces. The path-following control of a nonlinear vehicle model is implemented using a time-varying model predictive controller. A series of Kalman filters are used to represent the driver's ability to generate estimates of the system states from noisy sensory measurements, including the steering torque. It is found that under constant road friction conditions, the steering torque feedback reduces path-following errors provided the friction is sufficiently high to prevent frequent saturation of the tyres. When the driver model is extended to allow identification of, and adaptation to, a varying friction condition, it is found that the steering torque assists in the accurate identification of the friction condition. The simulation results give insight into the role of steering torque feedback arising from lateral tyre forces. The paper concludes with recommendations for further work. © 2011 Taylor & Francis.