11680467
Predictive unsupervised organisation in marine engine fault detection
This paper utilises topological learners, the self organising map in combination with the k means algorithm to organise potential engine faults and the respective location of faults, focussing on a 12 cylinder 2 stroke marine diesel engine. This method is applied to reduce the numerosity of the data presented to a user by selecting representative samples from a number of clusters to enable efficient diagnosis. The novelty of the approach centres around the sparsity of the dataset compared to the majority of fault diagnosis techniques, and the potential for improved safety and efficiency within the marine industry compared to existing diagnosis systems. The accuracy of the SOM and k means, as well as the neural gas algorithm is compared to the standard accuracy of the k means algorithm to validate the algorithmpsilas performance and application to this domain, where it can be seen that topological learners have much potential to be applied to the field of fault diagnosis.
35589713
Time Discretisation Applied to Anomaly Detection in a Marine Engine
An introduction to the problems associated with anomaly detection in a marine engine, explaining the benefits that the SAX representation brings to the field. Despite limitations in accuracy of the SAX representation in comparison with the normalised time series, we conclude that because of the reduction in data points that should be processed SAX should be considered further as a valid and efficient representation. Finally, a continuation of the work to make the approach more viable in the real world is briefly noted based upon Markov Chaining and Support Vector Machines.
18886117
An Effective Human Motion Classification Approach using Knowledge Representation in Qualitative Normalised Templates
Classification of human motion in video data is essential in numerous applications. However, problems arise as the human exhibits complex and dynamic motion that is nonlinear and time varying. In this paper, we propose a knowledge-based human motion classification framework that employs fuzzy qualitative reasoning to address these problems. Our approach utilises the rich contextual information (e.g. structural and transitional characteristic of human motion) captured in video sequence to effectively study and recognise human motion. With the aid of domain knowledge, a set of fuzzy rules are defined in the knowledge base. This work is in contrast with previous attempts that depend solely on the trajectories of the body parts. Experimental results on two classes of motion (e.g. walking and running) that result in similar motions; and a comparison with the conventional method has demonstrated and validated the effectiveness of the proposed method in improving the perception of human motion.
27826765
Analysing Flight Data Using Clustering Methods
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.
18397185
Motor Fault Diagnosis Based on Wavelet Energy and Immune Neural Network
Motor fault diagnosis methods are crucial in acquiring safe and reliable operation in motor drive systems. In this paper, a new method for the motor fault diagnosis is proposed based on wavelet packet transform (WPT) and artificial neural network (ANN). The energy of the vibration signals of motor can be obtained by the multi-decomposition of WPT and used as feature values of ANN inputs for fault diagnosis system. The artificial immune algorithm (AIA) for data clustering is employed to adaptively choose the centers and widths of the hidden layer centers of the radial basis function neural network (RBFNN). The simulation experiment results show the applicability and effectiveness of the proposed method to motor fault diagnosis.
37115701
Qualitative classification of descent phases in commercial flight data
Flight data from commercial aircraft in the descent is analysed using one-class classification techniques to identify possible unstable approaches. The method considers snapshots of flight parameters at certain heights in the descent and identifies any abnormalities. Combination rules are used to analyse the flight as a whole and it is found that it is possible to detect abnormal flights with a good degree of accuracy.
34349949
Text formats and web design for visually impaired and dyslexic readers - Clear Text for All
The Royal National Institute of the Blind (RNIB) has produced a Clear Print booklet, which contains recommendations for the production of Clear Print for the blind and partially sighted. The British Dyslexia Association (BDA) has produced a Dyslexia Style Guide, which covers similar issues. Both focus on producing text, which is clear and therefore more easily read, and there is significant overlap between the two. By comparing the two, a set of specifications for the production of text has been generated. Using the specifications should produce clear text for both dyslexic and visually impaired readers. It should improve readability for all. The text specifications plus additional recommendations from the BDA are considered with respect to an existing set of web site design guidelines for dyslexic readers to produce an enhanced set of guidelines compatible with both. These guidelines are recommended to be followed as standard, both for their benefits to visually impaired and dyslexic readers, promoting accessibility for these groups, and for their potential to improve accessibility for all.
1099339
Cognitive Scales and Mental Models for Inclusive Design
In keeping with a user capability and product demand approach to product assessment, this paper examines the cognitive demands placed on users when interacting with consumer products. The eventual aim is to develop a set of cognitive capability scales that could be used in the analytical evaluation of product interfaces. We explore the dimensions of cognitive capability relevant to product interaction and describe how these may be used to evaluate a given design. Planned work addresses quantitative measurement of cognitive capabilities and predictive validation of capability scales.
17110258
An ontology for scientific information in a Grid environment: the earth system Grid
In the emerging world of Grid Computing, shared computational, data, other distributed resources are becoming available to enable scientific advancement through collaborative research and collaboratories. This paper describes the increasing role of ontologies in the context of Grid Computing for obtaining, comparing and analyzing data. We present ontology entities and a declarative model that provide the outline for an ontology of scientific information. Relationships between concepts are also given. The implementation of some concepts described in this ontology is discussed within the context of the Earth System Grid II (ESG)[1].
367503
Large-Scale Dependency Knowledge Acquisition and its Extrinsic Evaluation Through Word Sense Disambiguation
Knowledge plays a central role in intelligent systems. Manual knowledge acquisition is very inefficient and expensive. In this paper, we present (1) an automatic method to acquire a large amount of lexical-dependency knowledge, and (2) an innovative knowledge representation model to effectively minimize the impact of noise and improve knowledge quality. We also propose a new type of knowledge base evaluation-extrinsic evaluation, which evaluates knowledge by its impact to an external application. In our experiments we adopt Word Sense Disambiguation (WSD) as the extrinsic evaluation measure. Due to the lack of sufficient knowledge, existing WSD methods either are brittle and only capable of processing a limited number of topics or words, or provide only mediocre performance in real-world settings. With the support of acquired knowledge, our unsupervised WSD system significantly outperformed the best unsupervised systems participating in SemEval 2007, and achieved the disambiguation accuracy approaching top-performing supervised systems.
10058829
A Fully Unsupervised Word Sense Disambiguation Method Using Dependency Knowledge
Word sense disambiguation is the process of determining which sense of a word is used in a given context. Due to its importance in understanding semantics of natural languages, word sense disambiguation has been extensively studied in Computational Linguistics. However, existing methods either are brittle and narrowly focus on specific topics or words, or provide only mediocre performance in real-world settings. Broad coverage and disambiguation quality are critical for a word sense disambiguation system. In this paper we present a fully unsupervised word sense disambiguation method that requires only a dictionary and unannotated text as input. Such an automatic approach overcomes the problem of brittleness suffered in many existing methods and makes broad-coverage word sense disambiguation feasible in practice. We evaluated our approach using SemEval 2007 Task 7 (Coarse-grained English All-words Task), and our system significantly outperformed the best unsupervised system participating in SemEval 2007 and achieved the performance approaching top-performing supervised systems. Although our method was only tested with coarse-grained sense disambiguation, it can be directly applied to fine-grained sense disambiguation.