Bayesian calibration for multiple source regression model

In large variety of practical applications, using information from different sources or different kind of data is a reasonable demand. The problem of studying multiple source data can be represented as a multi-task learning problem, and then the information from one source can help to study the information from the other source by extracting a shared common structure. From the other hand, parameter evaluations obtained from various sources can be confused and conflicting. This paper proposes a Bayesian based approach to calibrate data obtained from different sources and to solve nonlinear regression problem in the presence of heteroscedastisity of the multiple-source model. An efficient algorithm is developed for implementation. Using analytical and simulation studies, it is shown that the proposed Bayesian calibration improves the convergence rate of the algorithm and precision of the model. The theoretical results are supported by a synthetic example, and a real-world problem, namely, modeling unsteady pitching moment coefficient of aircraft, for which a recurrent neural network is constructed

Applying Bayesian networks to model uncertainty in project scheduling

PhDRisk Management has become an important part of Project Management. In spite of numerous advances in the field of Project Risk Management (PRM), handling uncertainty in complex projects still remains a challenge. An important component of Project Risk Management (PRM) is risk analysis, which attempts to measure risk and its impact on different project parameters such as time, cost and quality. By highlighting the trade-off between project parameters, the thesis concentrates on project time management under uncertainty. The earliest research incorporating uncertainty/risk in projects started in the late 1950’s. Since then, several techniques and tools have been introduced, and many of them are widely used and applied throughout different industries. However, they often fail to capture uncertainty properly and produce inaccurate, inconsistent and unreliable results. This is evident from consistent problems of cost and schedule overrun. The thesis will argue that the simulation-based techniques, as the dominant and state-of-the-art approach for modelling uncertainty in projects, suffers from serious shortcomings. More advanced techniques are required. Bayesian Networks (BNs), are a powerful technique for decision support under uncertainty that have attracted a lot of attention in different fields. However, applying BNs in project risk management is novel. The thesis aims to show that BN modelling can improve project risk assessment. A literature review explores the important limitations of the current practice of project scheduling under uncertainty. A new model is proposed which applies BNs for performing the famous Critical Path Method (CPM) calculation. The model subsumes the benefits of CPM while adding BN capability to properly capture different aspects of uncertainty in project scheduling

ComplexWorld Position Paper

The Complex ATM Position Paper is the common research vehicle that defines the high-level, strategic scientific vision for the ComplexWorld Network. The purpose of this document is to provide an orderly and consistent scientific framework for the WP-E complexity theme. The specific objectives of the position paper are to: - analyse the state of the art within the different research areas relevant to the network, identifying the major accomplishments and providing a comprehensive set of references, including the main publications and research projects; - include a complete list of , a list of application topics, and an analysis of which techniques are best suited to each one of those applications; - identify and perform an in-depth analysis of the most promising research avenues and the major research challenges lying at the junction of ATM and complex systems domains, with particular attention to their impact and potential benefits for the ATM community; - identify areas of common interest and synergies with other SESAR activities, with special attention to the research topics covered by other WP-E networks. An additional goal for future versions of this position paper is to develop an indicative roadmap on how these research challenges should be accomplished, providing a guide on how to leverage on different aspects of the complexity research in Air Transport

UAV Parameter Estimation with Gaussian Process Approximations

Unmanned Aerial Vehicles (UAVs) provide an alternative to manned aircraft for risk associated missions and applications where sizing constraints require miniaturized flying platforms. UAVs are currently utilised in an array of applications ranging from civilian research to military battlegrounds. A part of the development process for UAVs includes constructing a flight model. This model can be used for modern flight controller design and to develop high fidelity flight simulators. Furthermore, it also has a role in analysing stability, control and handling qualities of the platform. Developing such a model involves estimating stability and control parameters from flight data. These map the platform's control inputs to its dynamic response. The modeling process is labor intensive and requires coarse approximations. Similarly, models constructed through flight tests are only applicable to a narrow flight envelope and classical system identification approaches require prior knowledge of the model structure, which, in some instances may only be partially known. This thesis attempts to find a solution to these problems by introducing a new system identification method based on dependent Gaussian processes. The new method would allow for high fidelity non-linear flight dynamic models to be constructed through experimental data. The work is divided into two main components. The first part entails the development of an algorithm that captures cross coupling between input parameters, and learns the system stability and control derivatives. The algorithm also captures any dependencies embodied in the outputs. The second part focuses on reducing the heavy computational cost, which is a deterrent to learning the model from large test flight data sets. In addition, it explores the capabilities of the model to capture any non-stationary behavior in the aerodynamic coefficients. A modeling technique was developed that uses an additive sparse model to combine global and local Gaussian processes to learn a multi-output system. Having a combined approximation makes the model suitable for all regions of the flight envelope. In an attempt to capture the global properties, a new sampling method is introduced to gather information about the output correlations. Local properties were captured using a non-stationary covariance function with KD-trees for neighbourhood selection. This makes the model scalable to learn from high dimensional large-scale data sets. The thesis provides both theoretical underpinnings and practical applications of this approach. The theory was tested in simulation on a highly coupled oblique wing aircraft and was demonstrated on a delta-wing UAV platform using real flight data. The results were compared against an alternative parametric model and demonstrated robustness, improved identification of coupling between flight modes, sound ability to provide uncertainty estimates, and potential to be applied to a broader flight envelope

UAV Parameter Estimation with Gaussian Process Approximations

Unmanned Aerial Vehicles (UAVs) provide an alternative to manned aircraft for risk associated missions and applications where sizing constraints require miniaturized flying platforms. UAVs are currently utilised in an array of applications ranging from civilian research to military battlegrounds. A part of the development process for UAVs includes constructing a flight model. This model can be used for modern flight controller design and to develop high fidelity flight simulators. Furthermore, it also has a role in analysing stability, control and handling qualities of the platform. Developing such a model involves estimating stability and control parameters from flight data. These map the platform's control inputs to its dynamic response. The modeling process is labor intensive and requires coarse approximations. Similarly, models constructed through flight tests are only applicable to a narrow flight envelope and classical system identification approaches require prior knowledge of the model structure, which, in some instances may only be partially known. This thesis attempts to find a solution to these problems by introducing a new system identification method based on dependent Gaussian processes. The new method would allow for high fidelity non-linear flight dynamic models to be constructed through experimental data. The work is divided into two main components. The first part entails the development of an algorithm that captures cross coupling between input parameters, and learns the system stability and control derivatives. The algorithm also captures any dependencies embodied in the outputs. The second part focuses on reducing the heavy computational cost, which is a deterrent to learning the model from large test flight data sets. In addition, it explores the capabilities of the model to capture any non-stationary behavior in the aerodynamic coefficients. A modeling technique was developed that uses an additive sparse model to combine global and local Gaussian processes to learn a multi-output system. Having a combined approximation makes the model suitable for all regions of the flight envelope. In an attempt to capture the global properties, a new sampling method is introduced to gather information about the output correlations. Local properties were captured using a non-stationary covariance function with KD-trees for neighbourhood selection. This makes the model scalable to learn from high dimensional large-scale data sets. The thesis provides both theoretical underpinnings and practical applications of this approach. The theory was tested in simulation on a highly coupled oblique wing aircraft and was demonstrated on a delta-wing UAV platform using real flight data. The results were compared against an alternative parametric model and demonstrated robustness, improved identification of coupling between flight modes, sound ability to provide uncertainty estimates, and potential to be applied to a broader flight envelope