Localisation in 3D Images Using Cross-features Correlation Learning

Object detection and segmentation have evolved drastically over the past two decades thanks to the continuous advancement in the field of deep learning. Substantial research efforts have been dedicated towards integrating object detection techniques into a wide range of real-world prob-lems. Most existing methods take advantage of the successful application and representational ability of convolutional neural networks (CNNs). Generally, these methods target mainstream applications that are typically based on 2D imaging scenarios. Additionally, driven by the strong correlation between the quality of the feature embedding and the performance in CNNs, most works focus on design characteristics of CNNs, e.g., depth and width, to enhance their modelling capacity and discriminative ability. Limited research was directed towards exploiting feature-level dependencies, which can be feasibly used to enhance the performance of CNNs. More-over, directly adopting such approaches into more complex imaging domains that target data of higher dimensions (e.g., 3D multi-modal and volumetric images) is not straightforwardly appli-cable due to the different nature and complexity of the problem. In this thesis, we explore the possibility of incorporating feature-level correspondence and correlations into object detection and segmentation contexts that target the localisation of 3D objects from 3D multi-modal and volumetric image data. Accordingly, we first explore the detection problem of 3D solar active regions in multi-spectral solar imagery where different imaging bands correspond to different 2D layers (altitudes) in the 3D solar atmosphere.We propose a joint analysis approach in which information from different imaging bands is first individually analysed using band-specific network branches to extract inter-band features that are then dynamically cross-integrated and jointly analysed to investigate spatial correspon-dence and co-dependencies between the different bands. The aggregated embeddings are further analysed using band-specific detection network branches to predict separate sets of results (one for each band). Throughout our study, we evaluate different types of feature fusion, using convo-lutional embeddings of different semantic levels, as well as the impact of using different numbers of image bands inputs to perform the joint analysis. We test the proposed approach over different multi-modal datasets (multi-modal solar images and brain MRI) and applications. The proposed joint analysis based framework consistently improves the CNN’s performance when detecting target regions in contrast to single band based baseline methods.We then generalise our cross-band joint analysis detection scheme into the 3D segmentation problem using multi-modal images. We adopt the joint analysis principles into a segmentation framework where cross-band information is dynamically analysed and cross-integrated at vari-ous semantic levels. The proposed segmentation network also takes advantage of band-specific skip connections to maximise the inter-band information and assist the network in capturing fine details using embeddings of different spatial scales. Furthermore, a recursive training strat-egy, based on weak labels (e.g., bounding boxes), is proposed to overcome the difficulty of producing dense labels to train the segmentation network. We evaluate the proposed segmen-tation approach using different feature fusion approaches, over different datasets (multi-modal solar images, brain MRI, and cloud satellite imagery), and using different levels of supervisions. Promising results were achieved and demonstrate an improved performance in contrast to single band based analysis and state-of-the-art segmentation methods.Additionally, we investigate the possibility of explicitly modelling objective driven feature-level correlations, in a localised manner, within 3D medical imaging scenarios (3D CT pul-monary imaging) to enhance the effectiveness of the feature extraction process in CNNs and subsequently the detection performance. Particularly, we present a framework to perform the 3D detection of pulmonary nodules as an ensemble of two stages, candidate proposal and a false positive reduction. We propose a 3D channel attention block in which cross-channel informa-tion is incorporated to infer channel-wise feature importance with respect to the target objective. Unlike common attention approaches that rely on heavy dimensionality reduction and computa-tionally expensive multi-layer perceptron networks, the proposed approach utilises fully convo-lutional networks to allow directly exploiting rich 3D descriptors and performing the attention in an efficient manner. We also propose a fully convolutional 3D spatial attention approach that elevates cross-sectional information to infer spatial attention. We demonstrate the effectiveness of the proposed attention approaches against a number of popular channel and spatial attention mechanisms. Furthermore, for the False positive reduction stage, in addition to attention, we adopt a joint analysis based approach that takes into account the variable nodule morphology by aggregating spatial information from different contextual levels. We also propose a Zoom-in convolutional path that incorporates semantic information of different spatial scales to assist the network in capturing fine details. The proposed detection approach demonstrates considerable gains in performance in contrast to state-of-the-art lung nodule detection methods.We further explore the possibility of incorporating long-range dependencies between arbi-trary positions in the input features using Transformer networks to infer self-attention, in the context of 3D pulmonary nodule detection, in contrast to localised (convolutional based) atten-tion . We present a hybrid 3D detection approach that takes advantage of both, the Transformers ability in modelling global context and correlations and the spatial representational characteris-tics of convolutional neural networks, providing complementary information and subsequently improving the discriminative ability of the detection model. We propose two hybrid Transformer CNN variants where we investigate the impact of exploiting a deeper Transformer design –in which more Transformer layers and trainable parameters are incorporated– is used along with high-level convolutional feature inputs of a single spatial resolution, in contrast to a shallower Transformer design –of less Transformer layers and trainable parameters– while exploiting con-volutional embeddings of different semantic levels and relatively higher resolution.Extensive quantitative and qualitative analyses are presented for the proposed methods in this thesis and demonstrate the feasibility of exploiting feature-level relations, either implicitly or explicitly, in different detection and segmentation problems

Synthesis, characterisation and thermo-physical properties of highly stable graphene oxide-based aqueous nanofluids for low-temperature direct absorption solar collectors and solar still desalination.

Humanities’ insatiable demand for energy and fresh water has never been satisfied and continues to increase with an ever-increasing global population. Because both demands currently rely heavily on fossil fuels, the resulting detrimental consequences of rising greenhouse gas emissions, global warming and environmental degradation present major challenges for every nation. Importantly, the efficient conversion of energy to perform useful work is a factor that directly contributes to the financial development and economic sustainability of a country. Furthermore, the current global water demand is already much higher than the Earth’s natural water cycle can sustain, and this shortfall is presently being made up by the use of high energy consuming desalination processes. Thus, there is currently extensive research into developing eco-friendly and viable renewable energy sources to meet the demands for energy and fresh water. Solar energy offers an alternative energy source with the potential to alleviate the problems associated with fossil fuel-based energy generation and desalination. The performance of conventional solar thermal collectors is limited by poor optical properties and low thermal properties. Traditional solar stills are not widely used today, due to their poor thermal properties and low productivity levels. The thesis focuses on developing new and novel nanofluids based on novel graphene based materials with enhanced optical and thermal properties to improve the performance of direct absorption solar collectors (DASCs) and solar desalination stills. The thesis is arranged in two parts. The first part extensively reviews current solar collector systems for converting solar energy to thermal energy and also reviews current progress towards developing high thermal efficiency solar desalination systems. The second part presents actual case studies that evaluate the performance of newly developed novel graphene oxide and reduced graphene oxide based nanofluids for use in DASCs and solar stills

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

National freight transport planning: towards a Strategic Planning Extranet Decision Support System (SPEDSS)

This thesis provides a `proof-of-concept' prototype and a design architecture for a Object Oriented (00) database towards the development of a Decision Support System (DSS) for the national freight transport planning problem. Both governments and industry require a Strategic Planning Extranet Decision Support System (SPEDSS) for their effective management of the national Freight Transport Networks (FTN). This thesis addresses the three key problems for the development of a SPEDSS to facilitate national strategic freight planning: 1) scope and scale of data available and required; 2) scope and scale of existing models; and 3) construction of the software. The research approach taken embodies systems thinking and includes the use of: Object Oriented Analysis and Design (OOA/D) for problem encapsulation and database design; artificial neural network (and proposed rule extraction) for knowledge acquisition of the United States FTN data set; and an iterative Object Oriented (00) software design for the development of a `proof-of-concept' prototype. The research findings demonstrate that an 00 approach along with the use of 00 methodologies and technologies coupled with artificial neural networks (ANNs) offers a robust and flexible methodology for the analysis of the FTN problem domain and the design architecture of an Extranet based SPEDSS. The objectives of this research were to: 1) identify and analyse current problems and proposed solutions facing industry and governments in strategic transportation planning; 2) determine the functional requirements of an FTN SPEDSS; 3) perform a feasibility analysis for building a FTN SPEDSS `proof-of-concept' prototype and (00) database design; 4) develop a methodology for a national `internet-enabled' SPEDSS model and database; 5) construct a `proof-of-concept' prototype for a SPEDSS encapsulating identified user requirements; 6) develop a methodology to resolve the issue of the scale of data and data knowledge acquisition which would act as the `intelligence' within a SPDSS; 7) implement the data methodology using Artificial Neural Networks (ANNs) towards the validation of it; and 8) make recommendations for national freight transportation strategic planning and further research required to fulfil the needs of governments and industry. This thesis includes: an 00 database design for encapsulation of the FTN; an `internet-enabled' Dynamic Modelling Methodology (DMM) for the virtual modelling of the FTNs; a Unified Modelling Language (UML) `proof-of-concept' prototype; and conclusions and recommendations for further collaborative research are identified

Outlook for renewable energy technologies: Assessment of international programs and policies

The report presents an evaluation of worldwide research efforts in three specific renewable energy technologies, with a view towards future United States (US) energy security, environmental factors, and industrial competitiveness. The overall energy technology priorities of foreign governments and industry leaders, as well as the motivating factors for these priorities, are identified and evaluated from both technological and policy perspectives. The specific technologies of interest are wind, solar thermal, and solar photovoltaics (PV). These program areas, as well as the overall energy policies of Denmark, France, Germany, Italy, the United Kingdom (UK), Japan, Russia, and the European Community as a whole are described. The present and likely future picture for worldwide technological leadership in these technologies-is portrayed. The report is meant to help in forecasting challenges to US preeminence in the various technology areas, particularly over the next ten years, and to help guide US policy-makers as they try to identify specific actions which would help to retain and/or expand the US leadership position