Optimisation of hedging-integrated rule curves for reservoir operation

Reservoir managers use operational rule curves as guides for managing and operating reservoir systems. However, this approach saves no water for impending droughts, resulting in large shortages during such droughts. This problem can be tempered by integrating hedging with the rule curves to curtail the water releases during normal periods of operation and use the saved water to limit the amount and impact of water shortages during droughts. However, determining the timing and amount of hedging is a challenge. This thesis presents the application of genetic algorithms (GA) for the optimisation of hedging-integrated reservoir rule curves. However, due to the challenge of establishing the boundary of feasible region in standard GA (SGA), a new development of the GA i.e. the dynamic GA (DGA), is proposed. Both the new development and its hedging policies were tested through extensive simulations of the Ubonratana reservoir (Thailand). The first observation was that the new DGA was faster and more efficient than the SGA in arriving at an optimal solution. Additionally, the derived hedging policies produced significant changes in reservoir performance when compared to no-hedging policies. The performance indices analysed were reliability (time and volume), resilience, vulnerability and sustainability; the results showed that the vulnerability (i.e. average single periods shortage) in particular was significantly reduced with the optimised hedging rules as compared to using the no-hedging rule curves. This study also developed a monthly inflow forecasting model using artificial neural networks (ANN) to aid reservoir operational decision-making. Extensive testing of the model showed that it was able to provide inflow forecasts with reasonable accuracy. The simulated effect on reservoir performance of forecasted inflows vis-à-vis other assumed reservoir inflow knowledge situations showed that the ANN forecasts were superior, further reinforcing the importance of good inflow information for reservoir operation. The ability of hedging to harness the inherent buffering capacity of existing water resources systems for tempering water shortage (or vulnerability) without the need for expensive new-builds is a major outcome of this study. Although applied to Ubonratana, the study has utility for other regions of the world, where e.g. climate and other environmental changes are stressing the water availability situation

ARCHITECTURE OPTIMIZATION, TRAINING CONVERGENCE AND NETWORK ESTIMATION ROBUSTNESS OF A FULLY CONNECTED RECURRENT NEURAL NETWORK

Recurrent neural networks (RNN) have been rapidly developed in recent years. Applications of RNN can be found in system identification, optimization, image processing, pattern reorganization, classification, clustering, memory association, etc. In this study, an optimized RNN is proposed to model nonlinear dynamical systems. A fully connected RNN is developed first which is modified from a fully forward connected neural network (FFCNN) by accommodating recurrent connections among its hidden neurons. In addition, a destructive structure optimization algorithm is applied and the extended Kalman filter (EKF) is adopted as a network\u27s training algorithm. These two algorithms can seamlessly work together to generate the optimized RNN. The enhancement of the modeling performance of the optimized network comes from three parts: 1) its prototype - the FFCNN has advantages over multilayer perceptron network (MLP), the most widely used network, in terms of modeling accuracy and generalization ability; 2) the recurrency in RNN network make it more capable of modeling non-linear dynamical systems; and 3) the structure optimization algorithm further improves RNN\u27s modeling performance in generalization ability and robustness. Performance studies of the proposed network are highlighted in training convergence and robustness. For the training convergence study, the Lyapunov method is used to adapt some training parameters to guarantee the training convergence, while the maximum likelihood method is used to estimate some other parameters to accelerate the training process. In addition, robustness analysis is conducted to develop a robustness measure considering uncertainties propagation through RNN via unscented transform. Two case studies, the modeling of a benchmark non-linear dynamical system and a tool wear progression in hard turning, are carried out to testify the development in this dissertation. The work detailed in this dissertation focuses on the creation of: (1) a new method to prove/guarantee the training convergence of RNN, and (2) a new method to quantify the robustness of RNN using uncertainty propagation analysis. With the proposed study, RNN and related algorithms are developed to model nonlinear dynamical system which can benefit modeling applications such as the condition monitoring studies in terms of robustness and accuracy in the future

A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105

AbstractOptimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are required for the evaluation of parametric combinations in order to improve the quality of the machined parts and machining time. This problem could be categorized as the offline data-driven optimization problem. For such problems, the surrogate or predictive models are useful, which could be employed to approximate the objective functions for the optimization algorithms. This paper presents a data-driven surrogate-assisted optimizer to model the end mill cutting of aluminum alloy on a desktop milling machine. To facilitate that, material removal rate (MRR), surface roughness (Ra), and cutting forces are considered as the functions of tool diameter, spindle speed, feed rate, and depth of cut. The principal methodology is developed using a Bayesian regularized neural network (surrogate) and a beetle antennae search algorithm (optimizer) to perform the process optimization. The relationships among the process responses are studied using Kohonen’s self-organizing map. The proposed methodology is successfully compared with three different optimization techniques and shown to outperform them with improvements of 40.98% for MRR and 10.56% for Ra. The proposed surrogate-assisted optimization method is prompt and efficient in handling the offline machining data. Finally, the validation has been done using the experimental end milling cutting carried out on aluminum alloy to measure the surface roughness, material removal rate, and cutting forces using dynamometer for the optimal cutting parameters on desktop milling center. From the estimated surface roughness value of 0.4651 μm, the optimal cutting parameters have given a maximum material removal rate of 44.027 mm3/s with less amplitude of cutting force on the workpiece. The obtained test results show that more optimal surface quality and material removal can be achieved with the optimal set of parameters.</jats:p

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

An agent-independent task learning framework

We propose that for all situated agents, the process of task learning has many elements in common. A better understanding of these elements would be beneficial to both engineers attempting to design new agents for task learning and completion, and also to scientists seeking to better understand natural task learning. Therefore, this dissertation sets out our characterisation of agent-independent task learning, and explores its grounding in nature and utility in practise. We achieve this chiefly through the construction and demonstration of two novel task learning systems. Cross-Channel Observation and Imitation Learning (COIL; Wood and Bryson, 2007a,b) is our adaptation of Deb Roy’s Cross-Channel Early Lexical Learning System (CELL; Roy, 1999; Roy and Pentland, 2002) for agent-independent task learning by imitation. The General Task Learning Framework (GTLF) is built upon many of the principles learned through the development of COIL, and can additionally facilitate multi-modal, lifelong learning of complex skills and skill hierarchies. Both systems are validated through experiments conducted in the virtual reality-style game domain of Unreal Tournament (Digital Extremes, 1999). By applying agent-independent learning processes to virtual agents of this kind, we hope that researchers will be more inclined to consider them on a par with robots as tools for learning research.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Efficient Learning Machines

Computer scienc

Renewable Energy Resource Assessment and Forecasting

In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources

Advanced analytical methods for fraud detection: a systematic literature review

The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic

Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning

Learning is the ability to generalise beyond training examples; but because many generalisations are consistent with a given set of observations, all machine learning methods rely on inductive biases to select certain generalisations over others. This thesis explores how the model structure and priors affect the inductiven biases of probabilistic models, and our ability to learn and make inferences from data. Specifically we present theoretical analyses alongside algorithmic and modelling advances in three areas of probabilistic machine learning: sparse Gaussian process approximations and invariant covariance functions, learning flexible priors for variational autoencoders, and probabilistic approaches for few-shot learning. As inference is rarely tractable, we discuss variational inference methods as a secondary theme. First, we disentangle the theoretical properties and optimisation behaviour of two widely used sparse Gaussian process approximations. We conclude that a variational free energy approximation is more principled and extensible and should be used in practice despite potential optimisation difficulties. We then discuss how general symmetries and invariances can be integrated into Gaussian process priors and can be learned using the marginal likelihood. To make inference tractable, we develop a variational inference scheme that uses unbiased estimates of intractable covariance functions. We then address the mismatch between aggregate posteriors and priors in variational autoencoders and propose a mechanism to define flexible distributions using a form of rejection sampling. We use this approach to define a more flexible prior distribution on the latent space of a variational autoencoder, which generalises to unseen test data and reduces the number of low quality samples from the model in a practical way. Finally, we propose two probabilistic approaches to few-shot learning that achieve state of the art results on benchmarks, building on multi-task probabilistic models with adaptive classifier heads. Our first approach combines a pre-trained deep feature extractor with a simple probabilistic model for the head, and can be linked to automatically regularised softmax regression. The second employs an amortised head model; it can be viewed to meta-learn probabilistic inference for prediction, and can be generalised to other contexts such as few-shot regression.UK Engineering and Physics Research Council (EPSRC) DTA, Qualcomm Studentship in Technology, Max Planck Societ