36256315
Visual-Based View-Invariant Human Motion Analysis: A Review
This paper provides a comprehensive survey of research on view-invariant human motion analysis. Recent research has shown that view-invariant related issues has been one of the bottlenecks for human motion understanding. The priority in this paper has been given to view-invariant pose representation and estimation, behaviour understanding. Research challenges and future directions are discussed in the end.
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.
37865441
Adapting robot kinematics for human-arm motion recognition
This paper presents a novel method to the analysis of human-arm motion, in particular improving the efficiency of conventional motion recognition algorithms. Contrary to the prior art methods, this research develops a framework for human-arm motion recognition where qualitative normalised templates (QNTs) is proposed to replace the conventional approaches. First of all, the conventional robotic model has been employed to build a generic vision model for a human-arm, that is we utilise the robot kinematics to construct a stick model. Secondly, the qualitative robotic model is adopted to learn and construct the QNTs where human-arm motion is termed as, whose execution is consistent and could be easily characterised by a definite space-time trajectory in configuration space. Finally, classification of the human-arm motion is achieved by comparing the QNTs to the parameters learnt with particle filter based motion tracking algorithm. Experimental evaluation has demonstrated the effectiveness of the proposed method in human-arm motion classification, and our future work is focused on extending the proposed method to recognise complex human motion, e.g. walking and running.
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.
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.
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.
11160456
Structure of Lateral Inhibition in an Olfactory Bulb Model
It has been shown that mutual lateral inhibition of the projection neurones in the olfactory bulb, mediated by interneurones, serves to tune the representation of odours in the bulb and reduce the overlap between similaz odorants. In this paper we demonstrate that the parameters of the lateral interaction, specifically the relation of synaptic strength to cell separation and the effective overall gain of the network, have a significant effect on the strength and range of lateral inhibition in a simple model of the olfactory bulb.
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].
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.
7051215
Coefficient of variation of interspike intervals greater than 0.5. How and when?
Abstract. Using Stein's model with and without reversal potentials, we investigated the mechanism of production of spike trains with a CV (ISI) (standard deviation/mean interspike interval) greater than 0.5, as observed in the visual cortex. When the attractor of the deterministic part of the dynamics is below the firing threshold, spike generation results primarily from random fluctuations. Using computer simulation for a range of membrane decay times and with other model parameters set to values appropriate for the visual cortex, we demonstrate that CV (ISI) is then usually greater than 0.5; if the attractor is above the threshold, spike generation is mainly due to deterministic forces, and CV (ISI) is then usually lower than 0.5. The critical value of the inhibitory postsynaptic potential (IPSP) rate at which CV (ISI) becomes greater than 0.5 is determined, resulting in specifications of how neurones might adjust their synaptic inputs to elicit irregular spike trains.
9882695
Rigorously Defining and Analyzing Medical Processes: An Experience Report
This paper describes our experiences in defining the processes associated with preparing and administrating chemotherapy and then using those process definitions as the basis for analyses aimed at finding and correcting defects. The work is a collaboration between medical professionals from a major regional cancer center and computer science researchers. The work uses the Little-JIL language to create precise process definitions, the Propel system to specify precise process requirements, and the FLAVERS system to verify that the process definitions adhere to the requirement specifications. The paper describes how these technologies were applied to successfully identify defects in the chemotherapy process. Although this work is still ongoing, early experiences suggest that this approach can help reduce medical errors and improve patient safety. The work has also helped us to learn about the desiderata for process definition and analysis technologies, both of which are expected to be broadly applicable to other domains.