Model migration neural network for predicting battery aging trajectories

Accurate prediction of batteries’ future degradation is a key solution to relief users’ anxiety on battery lifespan and electric vehicle’s driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this paper, a feed-forward migration neural network is proposed to predict the batteries’ aging trajectories. Specifically, a base model that describes the capacity decay over time is first established from the existed battery aging dataset. This base model is then transformed by an input-output slope-and-bias-correction (SBC) method structure to capture the degradation of target cell. To enhance the model’s nonlinear transfer capability, the SBC-model is further integrated into a four-layer neural network, and easily trained via the gradient correlation algorithm. The proposed migration neural network is experimentally verified with four different commercial batteries. The predicted RMSEs are all lower than 2.5% when using only the first 30% of aging trajectories for neural network training. In addition, illustrative results demonstrate that a small size feed-forward neural network (down to 1-5-5-1) is sufficient for battery aging trajectory prediction

Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations

Initialization techniques for seasonal-to-decadal climate predictions fall into two main categories; namely full-field initialization (FFI) and anomaly initialization (AI). In the FFI case the initial model state is replaced by the best possible available estimate of the real state. By doing so the initial error is efficiently reduced but, due to the unavoidable presence of model deficiencies, once the model is let free to run a prediction, its trajectory drifts away from the observations no matter how small the initial error is. This problem is partly overcome with AI where the aim is to forecast future anomalies by assimilating observed anomalies on an estimate of the model climate. The large variety of experimental setups, models and observational networks adopted worldwide make it difficult to draw firm conclusions on the respective advantages and drawbacks of FFI and AI, or to identify distinctive lines for improvement. The lack of a unified mathematical framework adds an additional difficulty toward the design of adequate initialization strategies that fit the desired forecast horizon, observational network and model at hand. Here we compare FFI and AI using a low-order climate model of nine ordinary differential equations and use the notation and concepts of data assimilation theory to highlight their error scaling properties. This analysis suggests better performances using FFI when a good observational network is available and reveals the direct relation of its skill with the observational accuracy. The skill of AI appears, however, mostly related to the model quality and clear increases of skill can only be expected in coincidence with model upgrades. We have compared FFI and AI in experiments in which either the full system or the atmosphere and ocean were independently initialized. In the former case FFI shows better and longer-lasting improvements, with skillful predictions until month 30. In the initialization of single compartments, the best performance is obtained when the stabler component of the model (the ocean) is initialized, but with FFI it is possible to have some predictive skill even when the most unstable compartment (the extratropical atmosphere) is observed. Two advanced formulations, least-square initialization (LSI) and exploring parameter uncertainty (EPU), are introduced. Using LSI the initialization makes use of model statistics to propagate information from observation locations to the entire model domain. Numerical results show that LSI improves the performance of FFI in all the situations when only a portion of the system's state is observed. EPU is an online drift correction method in which the drift caused by the parametric error is estimated using a short-time evolution law and is then removed during the forecast run. Its implementation in conjunction with FFI allows us to improve the prediction skill within the first forecast year. Finally, the application of these results in the context of realistic climate models is discussed

GARDSim - A GPS Receiver Simulation Environment for Integrated Navigation System Development and Analysis

Airservices Australia has recently proposed the use of a Ground-based Regional Augmentation System (GRAS) to improve the safety of using the NAVSTAR Global Positioning System (GPS) in aviation. The GRAS Airborne Receiver Development project (GARD) is being conducted by QUT in conjunction with Airservices Australia and GPSat Systems. The aim of the project is to further enhance the safety and reliability of GPS and GRAS by incorporating smart sensor technology including advanced GPS signal processing and Micro-Electro-Mechanical-Sensor (MEMS) based inertial components. GARDSim is a GPS and GRAS receiver simulation environment which has been developed for algorithm development and analysis in the GARD project. GARDSim is capable of simulating any flight path using a given aeroplane flight model, simulating various GPS, GRAS and inertial system measurements and performing high integrity navigation solutions for the flight. This paper discusses the architecture and capabilities of GARDSim. Simulation results will be presented to demonstrate the usefulness of GARDSim as a simulation environment for algorithm development and evaluation

Medicines in parallel trade in the European Union: a gravity specification

While recent research has explored the phenomenon of drug parallel trade in regulated environments such as the European Union (EU), or the European Economic Area, little is known about the mechanisms that explain its origin or the role of the distribution chain in exporting and importing countries in determining its extent. By building on theoretical literature explaining the role of the distribution chain, this paper draws on an empirical specification of a gravity model to examine the determinants of inter-country flows of parallel-traded drugs. In this context, the paper deals with the effect of differences in the regulation of and competition in the distribution chain in the countries of origin and destination. The paper draws on proprietary data from the Intercontinental Medical Statistics database (for the Netherlands and other EU countries that export to the Netherlands) which identify the country of origin of parallel-imported medicines from 1997-2002 for a therapeutic group (statins) for which there is no generic competition. The study reveals that although parallel trade is a specific form of arbitrage, it is primarily a regulation-induced phenomenon. As a result, although the driving force for parallel trade is price differences across countries, the propagation mechanism lies in (a) the way drug prices are regulated across countries and (b) fragmentation and the underlying incentive structure in the wholesale distribution chain in countries where drug prices are regulated. The implications that flow from our study are that a more flexible and competitive and less fragmented (along national borders) distribution chain, particularly at wholesale level, might reduce the extent of and potential for parallel trade

Discrete Imaging Models for Three-Dimensional Optoacoustic Tomography using Radially Symmetric Expansion Functions

Optoacoustic tomography (OAT), also known as photoacoustic tomography, is an emerging computed biomedical imaging modality that exploits optical contrast and ultrasonic detection principles. Iterative image reconstruction algorithms that are based on discrete imaging models are actively being developed for OAT due to their ability to improve image quality by incorporating accurate models of the imaging physics, instrument response, and measurement noise. In this work, we investigate the use of discrete imaging models based on Kaiser-Bessel window functions for iterative image reconstruction in OAT. A closed-form expression for the pressure produced by a Kaiser-Bessel function is calculated, which facilitates accurate computation of the system matrix. Computer-simulation and experimental studies are employed to demonstrate the potential advantages of Kaiser-Bessel function-based iterative image reconstruction in OAT