Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

Imperial Users onl

Advanced Sensing, Fault Diagnostics, and Structural Health Management

Advanced sensing, fault diagnosis, and structural health management are important parts of the maintenance strategy of modern industries. With the advancement of science and technology, modern structural and mechanical systems are becoming more and more complex. Due to the continuous nature of operation and utilization, modern systems are heavily susceptible to faults. Hence, the operational reliability and safety of the systems can be greatly enhanced by using the multifaced strategy of designing novel sensing technologies and advanced intelligent algorithms and constructing modern data acquisition systems and structural health monitoring techniques. As a result, this research domain has been receiving a significant amount of attention from researchers in recent years. Furthermore, the research findings have been successfully applied in a wide range of fields such as aerospace, manufacturing, transportation and processes

Machine learning solutions for maintenance of power plants

The primary goal of this work is to present analysis of current market for predictive maintenance software solutions applicable to a generic coal/gas-fired thermal power plant, as well as to present a brief discussion on the related developments of the near future. This type of solutions is in essence an advanced condition monitoring technique, that is used to continuously monitor entire plants and detect sensor reading deviations via correlative calculations. This approach allows for malfunction forecasting well in advance to a malfunction itself and any possible unforeseen consequences. Predictive maintenance software solutions employ primitive artificial intelligence in the form of machine learning (ML) algorithms to provide early detection of signal deviation. Before analyzing existing ML based solutions, structure and theory behind the processes of coal/gas driven power plants is going to be discussed to emphasize the necessity of predictive maintenance for optimal and reliable operation. Subjects to be discussed are: basic theory (thermodynamics and electrodynamics), primary machinery types, automation systems and data transmission, typical faults and condition monitoring techniques that are also often used in tandem with ML. Additionally, the basic theory on the main machine learning techniques related to malfunction prediction is going to be briefly presented

Earth Resources, A Continuing Bibliography with Indexes

This bibliography lists 460 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1984. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

Intelligent Microwave Detection of Surface and Sub-Surface Anomalies

Microwave near-field testing is a promising nondestructive testing method because of its unique capability to interrogate metallic surfaces and multi-layer dielectric structures. Due to today's need for lighter, stronger, and more-durable materials, enhanced dielectrics are increasingly being used to replace or coat metals. Consequently, conventional testing methods, with their limited penetration, are no longer adequate, but microwave testing sensors transmit signals that can penetrate into dielectrics and so detect surface and subsurface anomalies. Due to the growing use of microwave near-field sensors for different daily life applications, there is an ongoing need to improve their performance. Recently, artificial engineered electromagnetic materials (metamaterials) have been utilized to demonstrate strong localization and enhancement of electrical fields around sensing elements in order to improve probes sensitivities. Metamaterials are being used to enhance sensors design at the hardware level for better anomaly and flaw detection. Currently, microwave sensors are being used to capture large and complex information, but doing so requires better integration of signal processing methods. Implementing artificial intelligence algorithms to process information collected by microwave sensors can address the challenge associated with information complexity or obscure pattern changes. To address this gap in microwave near-field evaluation, this study integrates machine learning techniques with microwave near-field testing. Machine learning is a subset of artificial intelligence that denotes a set of methods that can automatically detect patterns in data to build a learning model. The learned model is then used for decision making about unseen data. Employing machine learning techniques for building classification models, this work combines machine learning algorithms with microwave near-field testing. In particular, it aims to build machine learning models that enhance flaw and anomaly detection in microwave near-field testing. The trained machine models can be integrated or embedded in a portable device or rack mounted microwave near-field testing equipment. The value of this approach is confirmed through numerical simulations and laboratory measurements

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations