34728116
Travel Speed Prediction Using Fuzzy Reasoning
The speed prediction algorithm introduced in this paper takes advantage of fuzzy systems that are insensitive to random noise, robust to uncertainties, and transparent to interpretation. The proposed algorithm for outlier detection selects the potential outliers based on the density rather than the deviation adopted in conventional approaches. To evaluate the developed system, a seris of experiments conducted on the real world data. The result of the comparison performed to evaluate the outliler detection method proposed reveals the benefit from the consideration of density. The cross validation results indicate the effectiveness of the fuzzy inference system developed.
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.
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.
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.
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.
4634224
Structural analysis of the KGD sequence loop of barbourin, an αIIbβ3-specific disintegrin
Disintegrins constitute a class of small proteins that inhibit platelet aggregation by binding to the fibrinogen receptor, also referred to as integrin αIIbβ3. Contrarily to other disintegrins that bind to a series of integrins via their Arg-Gly-Asp domain, the recognition site of barbourin contains a Lys-Gly-Asp sequence that ensures its specificity towards αIIbβ3. In this article, a three-dimensional model of barbourin is proposed using homology modeling and large-scale molecular dynamics simulations. The conformations of the Lys-Gly-Asp sequence of barbourin are analyzed and compared to those of peptidomimetics that exhibit similar specificity towards αIIbβ3. The tryptophan residue following the Lys-Gly-Asp sequence of the binding domain is shown to play a crucial role in the biological activity and the specificity of barbourin. Our results suggest that this disintegrin anchors to the binding pocket of the γ-chain of fibrinogen rather than to those of the Arg-Gly-Asp sequence.
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.
60537960
An orthodontic expert system
Abstract A Fuzzy Relational Inference Language, FRIL has been used to generate an expert system to help non-specialist dentists with orthodontic problems. The use of fuzzy relations, descriptors and numbers has been found useful in modelling the thought processes of orthodontic specialists.
1440521
Spike synchronization in a biophysically-detailed model of the olfactory bulb
Abstract Stimulus-evoked synchronization of action potentials has been demonstrated in mammalian olfactory bulb and in insect antennal lobes. Abolition of synchronization has been shown to impair the ability of honeybees to perform fine olfactory discrimination. We present a biophysically detailed computer model of the olfactory bulb which qualitatively reproduces many features seen in experimental recordings. The mitral cells of the model synchronize readily without common input due to lateral interactions with inhibitory granule cells. Weakly activated mitral cells fire more slowly than, but always synchronously with, strongly activated cells. Nearby cells synchronize more readily than widely separated ones.
863031
Component Selection to Optimize Distance Function Learning in Complex Scientific Data Sets
Analyzing complex scientific data, e.g., graphs and images, often requires comparison of features: regions on graphs, visual aspects of images and related metadata, some features being relatively more important. The notion of similarity for comparison is typically distance between data objects which could be expressed as distance between features. We refer to distance based on each feature as a component. Weights of components representing relative importance of features could be learned using distance function learning algorithms. However, it is seldom known which components optimize learning, given criteria such as accuracy, efficiency and simplicity. This is the problem we address. We propose and theoretically compare four component selection approaches: Maximal Path Traversal, Minimal Path Traversal, Maximal Path Traversal with Pruning and Minimal Path Traversal with Pruning. Experimental evaluation is conducted using real data from Materials Science, Nanotechnology and Bioinformatics. A trademarked software tool is developed as a highlight of this work.