Common and Unique Feature Learning for Data Fusion

University of Technology Sydney. Faculty of Engineering and Information Technology.In today’s era of big data, information about a phenomenon of interest is available from multiple acquisitions. Data captured from each of these acquisition frameworks are commonly known as modality, where each modality provides information in a complementary manner. Despite the evident benefits and plethora of works on data fusion, two challenging issues persist, 1) feature representation: how to exploit the data diversity that multiple modalities offer, and 2) feature fusion: how to combine the heterogeneous information for better decision making. To address these challenges, this thesis presents a significantly improved model of two widely utilised fusion techniques, a) early fusion: combining features from multiple modalities for joint prediction, and b) late fusion: combining modality-specific predictions at the decision level. I illustrate how both these techniques have their own specific limitations, with late fusion unable to harness the inter-modality benefits, and the reliance of early fusion on a single model causing failure when information from any modality is futile. To overcome these drawbacks, I developed novel multimodal systems that performs feature extraction and feature fusion in a consolidated frameworks. Technically, I designed feature extraction schemes to capture both unique information from individual modalities and common information from multimode representations. I then combine these two kinds of information for supervised prediction, by designing efficient fusion schemes that enable this frameworks to perform information discovery and feature fusion simultaneously. In this thesis, I also demonstrated the benefits of fusing both the common and unique information in supervised learning and validate the significance of the developed techniques on multimodal, multiview, and multisource datasets. The designed methods leverage the multimodal benefits by creating additional diversity, and obtain a more unified view of the underlying phenomenon for better decision making

A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics

Graph neural networks (GNNs) have demonstrated a significant boost in prediction performance on graph data. At the same time, the predictions made by these models are often hard to interpret. In that regard, many efforts have been made to explain the prediction mechanisms of these models from perspectives such as GNNExplainer, XGNN and PGExplainer. Although such works present systematic frameworks to interpret GNNs, a holistic review for explainable GNNs is unavailable. In this survey, we present a comprehensive review of explainability techniques developed for GNNs. We focus on explainable graph neural networks and categorize them based on the use of explainable methods. We further provide the common performance metrics for GNNs explanations and point out several future research directions

Construction of Data Driven Decomposition Based Soft Sensors with Auto Encoder Deep Neural Network for IoT Healthcare Applications

The architecture of IoT healthcare is motivated towards the data-driven realization and patient-centric health models, whereas the personalized assistance is provided by deploying the advanced sensors. According to the procedures in surgery, in the emergency unit, the patients are monitored till they are stable physically and then shifted to ward for further recovery and evaluation. Normally evaluation done in ward doesn’t suggest continuous parameters monitoring for physiological condition and thus relapse of patients are common. In real-time healthcare applications, the vital parameters will be estimated through dedicated sensors, that are still luxurious at the present situation and highly sensitive to harsh conditions of environment. Furthermore, for real-time monitoring, delay is usually present in the sensors. Because of these issues, data-driven soft sensors are highly attractive alternatives. This research is motivated towards this fact and Auto Encoder Deep Neural Network (AutoEncDeepNN) is proposed depending on Health Framework in the internet assisting the patients with trigger-based sensor activation model to manage master and slave sensors. The advantage of the proposed method is that the hidden information are mined automatically from the sensors and high representative features are generated by multiple layer’s iteration. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like Hierarchical Extreme Learning Machine (HELM), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). It is found that the proposed AutoEncDeepNN method achieves 94.72% of accuracy, 41.96% of RMSE, 34.16% of RAE and 48.68% of MAE in 74.64 ms

LARGE-SCALE POWER TRANSMISSION SYSTEMS' INTEGRATED ELECTRIC VEHICLE LOAD MODELLING

A variety of Electric Vehicle (EV) charging algorithms provide various EV charging load profiles, when utilized together, has an impact on the electrical grid functions. Present-day charging an EV Models of demand are either based on level of charging when an EV arrives or smart charging algorithms strengthened with specific charging levels and/or procedures. In this work, a brand-new data-driven technique for calculating EV charging load is suggested. They start by introducing a mathematical model that describes an adaptability of demand for EV charging. The characteristics of several EV load models are then identified, and advanced simulation techniques are suggested to simulate EV charging demand under various power market realizations. The suggested EV load modeling technique may act as a benchmark system by simulating various EV operating schedules, charging levels, and consumer engagement. The suggested framework would also give EV charging infrastructure advice from transmission system operators development in contemporary power networks

SMART GRID ENERGY PRODUCTION AND TRANSMISSION SYSTEM MODELING AND COMPUTATIONAL ASSESSMENT METHODS

Based on the continuous growth of the economy, widespread adoption of intermittent renewable energy sources, and extensive use of information and communication technologies, conventional electric power systems are no longer able to meet the enormous demands of the information age. Diverse renewable energy technologies have been quickly developed to address the energy issue and environmental damage. However, since renewable energy sources are unpredictable and erratic, the widespread use of different renewable energy technologies has consequently put significant strain on the security and dependability of conventional power networks. The Smart Grid (SG) is a modernized electrical network that makes use of cutting-edge communication, control, and information technology to facilitate the integration of renewable energy sources, increase energy efficiency, and improve dependability and security. The invention of computational modeling and evaluation methodologies for SG energy transmission and production networks is the main topic of the research. The Internet of Energy (IoE), which will eventually replace the conventional power production and distribution networks, increases the need to be familiar with the proper computing tools in order to conduct any future SG investigation. The software for simulation that is significant to the modeling and analysis of electrical power production, transmission, distribution, and related systems is examined in this research. The study's conclusions are anticipated to aid in the creation of power generation and transmission systems that are more effective, dependable, and sustainable

Modelling liquid film in modern GDI engines and the impact on particulate matter emissions, Part 1

This paper presents the details of a Computational Fluid Dynamics methodology to accurately model the process of mixture preparation in modern Gasoline Direct Injection engines, with particular emphasis on liquid film as one of the main causes of Particulate Matter formation. The proposed modelling protocol, centred on the Bai-Onera approach of droplets-wall interaction and on multi-component surrogate fuel blend models, is validated against relevant published data and then applied to a modern small-capacity GDI engine, featuring centrally-mounted spray-guided injection system. The work covers a range of part-load, stoichiometric and theoretically-homogeneous operating conditions, for which experimental engine data and engine-out Particle Number measurements were available. The results, based on the parametric variation of start of injection timing and injection pressure, demonstrate how both fuel mal-distribution and liquid film retained at spark timing, may contribute to PN emissions, whilst their relative importance vary depending on operating conditions and engine control strategy. Control of PN emissions and compliance with future, more stringent regulations remain large challenges for the engine industry. Renewed and disruptive approaches, which also consider the sustainability of the sector, appear to be essential. This work, developed using Siemens Simcenter CFD software as part of the Ford-led APC6 DYNAMO project, aims to contribute to the development of a reliable and cost-effective digital toolset, which supports engine development and diagnostics through a more fundamental assessment of engine operation and emissions formation

A low cost adaptive balance tanning platform for stroke patients

by Sunny VermaM.Tech