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Abstract-Unmanned Surface Vehicle (USV) is an important application of unmanned systems and these USVs provide safe and secure operation in hostile environments. But these USVs are highly reliant on their positioning system such as Global Position System (GPS) and loss of positioning information from GPS can cause catastrophe. To overcome this positioning challenge for a USV under GPS denial environment, we propose a real-time positioning algorithm based on radar and satellite images to determine the USV position. The algorithm takes coastline as a registration feature to implement an image registration between a horizontal viewing angle image from a radar and a vertical viewing angle image from a satellite. The contributions of this paper consist of two parts. Firstly, a coastline feature extraction method based on edge gray features for both radar and satellite images is provided. Secondly, a high efficiency image registration method which takes the dimensionality reduction distance as an indicator was proposed for USV embedded system. The results from six typical application scenarios show that the maximum positioning error of the proposed algorithm is 28.02 m under the worst case. A continuous positioning experiment shows that the average error of the algorithm is 9.77m, which indicates that the algorithm can meet the positioning requirements of a USV under GPS denial environment.

Real-time, accurate, and robust localisation is critical for autonomous vehicles (AVs) to achieve safe, efficient driving, whilst real-time performance is essential for AVs to achieve their current position in time for decision making. To date, no review paper has quantitatively compared the real-time performance between different localisation techniques based on various hardware platforms and programming languages and analysed the relations among localisation methodologies, real-time performance and accuracy. Therefore, this paper discusses the state-of-the-art localisation techniques and analyses their overall performance in AV application. For further analysis, this paper firstly proposes a localisation algorithm operations capability (LAOC)-based equivalent comparison method to compare the relative computational complexity of different localisation techniques; then, it comprehensively discusses the relations among methodologies, computational complexity, and accuracy. Analysis results show that the computational complexity of localisation approaches differs by a maximum of about times, whilst accuracy varies by about 100 times. Vision-and data fusion-based localisation techniques have about 2-5 times potential for improving accuracy compared with lidar-based localisation. Lidar-and vision-based localisation can reduce computational complexity by improving image registration method efficiency. Data fusion-based localisation can achieve better real-time performance compared with lidar-and vision-based localisation because each standalone sensor does not need to develop a complex algorithm to achieve its best localisation potential. Vehicle-toeverything (V2X) technology can improve positioning robustness. Finally, the potential solutions and future orientations of AVs' localisation based on the quantitative comparison results are discussed.

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This paper reviews existing forms of density-based, partitional and hierarchical clustering methods in the context of flight data analysis. Advantages and disadvantages are fully explored with a focus on proposing a clustering-based ensemble framework for monitoring flight data in order to search for anomalies during flight operation. Case studies in selected flight scenarios are provided to demonstrate the potential of clustering methods and their integration with reasoning techniques in detecting abnormal flights.

Huge advances in peer-to-peer systems and attempts to develop the semantic web have revealed a critical issue in information systems across multiple domains: the absence of semantic interoperability. Today, businesses operating in a digital environment require increased supply-chain automation, interoperability, and data governance. While research on the semantic web and interoperability has recently received much attention, a dearth of studies investigates the relationship between these two concepts in depth. To address this knowledge gap, the objective of this study is to conduct a review and bibliometric analysis of 3511 Scopus-registered papers on the semantic web and interoperability published over the past two decades. In addition, the publications were analyzed using a variety of bibliometric indicators, such as publication year, journal, authors, countries, and institutions. Keyword co-occurrence and co-citation networks were utilized to identify the primary research hotspots and group the relevant literature. The findings of the review and bibliometric analysis indicate the dominance of conference papers as a means of disseminating knowledge and the substantial contribution of developed nations to the semantic web field. In addition, the keyword co-occurrence network analysis reveals a significant emphasis on semantic web languages, sensors and computing, graphs and models, and linking and integration techniques. Based on the co-citation clustering, the Internet of Things, semantic web services, ontology mapping, building information modeling, bioinformatics, education and e-learning, and semantic web languages were identified as the primary themes contributing to the flow of knowledge and the growth of the semantic web and interoperability field. Overall, this review substantially contributes to the literature and increases scholars’ and practitioners’ awareness of the current knowledge composition and future research directions of the semantic web field.

This paper presents a novel two phase method that combines one class support vector machine classifiers classifiers using combination rules to quantitatively assess the degree of abnormality at various heights during individual aircraft descents and also over the whole descent. Whilst classifiers have been combined before in the literature with success, it is the first time they have been applied to the problem of analysing the act of descending of commercial jet aircraft. The method is tested on artificial Gaussian data and flight data from an industrial partner, Flight Data Services Ltd, the world's leading flight data analysis provider, with promising results.

Although significant research has been undertaken to reduce high level energy consumption in a data centre, there has been very little focus on reducing storage drive energy consumption via the intelligent allocation of workload commands at the file system level. This paper presents a method for optimising drive energy consumption within a custom built storage cluster containing multiple drives, using multi-objective goal attainment optimization.