5,825 research outputs found
Machine-Part cell formation through visual decipherable clustering of Self Organizing Map
Machine-part cell formation is used in cellular manufacturing in order to
process a large variety, quality, lower work in process levels, reducing
manufacturing lead-time and customer response time while retaining flexibility
for new products. This paper presents a new and novel approach for obtaining
machine cells and part families. In the cellular manufacturing the fundamental
problem is the formation of part families and machine cells. The present paper
deals with the Self Organising Map (SOM) method an unsupervised learning
algorithm in Artificial Intelligence, and has been used as a visually
decipherable clustering tool of machine-part cell formation. The objective of
the paper is to cluster the binary machine-part matrix through visually
decipherable cluster of SOM color-coding and labelling via the SOM map nodes in
such a way that the part families are processed in that machine cells. The
Umatrix, component plane, principal component projection, scatter plot and
histogram of SOM have been reported in the present work for the successful
visualization of the machine-part cell formation. Computational result with the
proposed algorithm on a set of group technology problems available in the
literature is also presented. The proposed SOM approach produced solutions with
a grouping efficacy that is at least as good as any results earlier reported in
the literature and improved the grouping efficacy for 70% of the problems and
found immensely useful to both industry practitioners and researchers.Comment: 18 pages,3 table, 4 figure
Visualising the structure of document search results: A comparison of graph theoretic approaches
This is the post-print of the article - Copyright @ 2010 Sage PublicationsPrevious work has shown that distance-similarity visualisation or ‘spatialisation’ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or ‘cluster growing’ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic owing to their inherent high dimensionality and non-linearity. Conventional linear approaches to dimension reduction tend to fail at this kind of task, sacrificing local structural in order to preserve a globally optimal mapping. In this paper the clustering performance of a recently proposed algorithm called isometric feature mapping (Isomap), which deals with non-linearity by transforming dissimilarities into geodesic distances, is compared to that of non-metric multidimensional scaling (MDS). Various graph pruning methods, for geodesic distance estimation, are also compared. Results show that Isomap is significantly better at preserving local structural detail than MDS, suggesting it is better suited to cluster growing and other semantic navigation tasks. Moreover, it is shown that applying a minimum-cost graph pruning criterion can provide a parameter-free alternative to the traditional K-neighbour method, resulting in spatial clustering that is equivalent to or better than that achieved using an optimal-K criterion
Two-dimensional homography-based correction of positional errors in widefield MRT images
A steradian of the southern sky has been imaged at 151.5 MHz using the
Mauritius Radio Telescope (MRT). These images show systematics in positional
errors of sources when compared to source positions in the Molonglo Reference
Catalogue (MRC). We have applied two-dimensional homography to correct for
systematic positional errors in the image domain and thereby avoid
re-processing the visibility data. Positions of bright (above 15-{\sigma})
point sources, common to MRT catalogue and MRC, are used to set up an
over-determined system to solve for the homography matrix. After correction the
errors are found to be within 10% of the beamwidth for these bright sources and
the systematics are eliminated from the images. This technique will be of
relevance to the new generation radio telescopes where, owing to huge data
rates, only images after a certain integration would be recorded as opposed to
raw visibilities. It is also interesting to note how our investigations cued to
possible errors in the array geometry. The analysis of positional errors of
sources showed that MRT images are stretched in declination by ~1 part in 1000.
This translates to a compression of the baseline scale in the visibility
domain. The array geometry was re-estimated using the astrometry principle. The
estimates show an error of ~1 mm/m, which results in an error of about half a
wavelength at 150 MHz for a 1 km north-south baseline. The estimates also
indicate that the east-west arm is inclined by an angle of ~40 arcsec to the
true east-west direction.Comment: 9 pages, 8 figures, accepted for publication in MNRA
The State-of-the-Art of Set Visualization
Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net
Adaptive heterogeneous parallelism for semi-empirical lattice dynamics in computational materials science.
With the variability in performance of the multitude of parallel environments available today, the conceptual overhead created by the need to anticipate runtime information to make design-time decisions has become overwhelming. Performance-critical applications and libraries carry implicit assumptions based on incidental metrics that are not portable to emerging computational platforms or even alternative contemporary architectures. Furthermore, the significance of runtime concerns such as makespan, energy efficiency and fault tolerance depends on the situational context. This thesis presents a case study in the application of both Mattsons prescriptive pattern-oriented approach and the more principled structured parallelism formalism to the computational simulation of inelastic neutron scattering spectra on hybrid CPU/GPU platforms. The original ad hoc implementation as well as new patternbased and structured implementations are evaluated for relative performance and scalability. Two new structural abstractions are introduced to facilitate adaptation by lazy optimisation and runtime feedback. A deferred-choice abstraction represents a unified space of alternative structural program variants, allowing static adaptation through model-specific exhaustive calibration with regards to the extrafunctional concerns of runtime, average instantaneous power and total energy usage. Instrumented queues serve as mechanism for structural composition and provide a representation of extrafunctional state that allows realisation of a market-based decentralised coordination heuristic for competitive resource allocation and the Lyapunov drift algorithm for cooperative scheduling
Learning Incoherent Subspaces: Classification via Incoherent Dictionary Learning
In this article we present the supervised iterative projections and rotations (s-ipr) algorithm, a method for learning discriminative incoherent subspaces from data. We derive s-ipr as a supervised extension of our previously proposed iterative projections and rotations (ipr) algorithm for incoherent dictionary learning, and we employ it to learn incoherent sub-spaces that model signals belonging to different classes. We test our method as a feature transform for supervised classification, first by visualising transformed features from a synthetic dataset and from the ‘iris’ dataset, then by using the resulting features in a classification experiment
Making nonlinear manifold learning models interpretable: The manifold grand tour
Dimensionality reduction is required to produce visualisations of high dimensional data. In this framework, one of the most straightforward approaches to visualising high dimensional data is based on reducing complexity and applying linear projections while tumbling the projection axes in a defined sequence which generates a Grand Tour of the data. We propose using smooth nonlinear topographic maps of the data distribution to guide the Grand Tour, increasing the effectiveness of this approach by prioritising the linear views of the data that are most consistent with global data structure in these maps. A further consequence of this approach is to enable direct visualisation of the topographic map onto projective spaces that discern structure in the data. The experimental results on standard databases reported in this paper, using self-organising maps and generative topographic mapping, illustrate the practical value of the proposed approach. The main novelty of our proposal is the definition of a systematic way to guide the search of data views in the grand tour, selecting and prioritizing some of them, based on nonlinear manifold models
Self- and Super-organizing Maps in R: The kohonen Package
In this age of ever-increasing data set sizes, especially in the natural sciences, visualisation becomes more and more important. Self-organizing maps have many features that make them attractive in this respect: they do not rely on distributional assumptions, can handle huge data sets with ease, and have shown their worth in a large number of applications. In this paper, we highlight the kohonen package for R, which implements self-organizing maps as well as some extensions for supervised pattern recognition and data fusion.
Soft clustering analysis of galaxy morphologies: A worked example with SDSS
Context: The huge and still rapidly growing amount of galaxies in modern sky
surveys raises the need of an automated and objective classification method.
Unsupervised learning algorithms are of particular interest, since they
discover classes automatically. Aims: We briefly discuss the pitfalls of
oversimplified classification methods and outline an alternative approach
called "clustering analysis". Methods: We categorise different classification
methods according to their capabilities. Based on this categorisation, we
present a probabilistic classification algorithm that automatically detects the
optimal classes preferred by the data. We explore the reliability of this
algorithm in systematic tests. Using a small sample of bright galaxies from the
SDSS, we demonstrate the performance of this algorithm in practice. We are able
to disentangle the problems of classification and parametrisation of galaxy
morphologies in this case. Results: We give physical arguments that a
probabilistic classification scheme is necessary. The algorithm we present
produces reasonable morphological classes and object-to-class assignments
without any prior assumptions. Conclusions: There are sophisticated automated
classification algorithms that meet all necessary requirements, but a lot of
work is still needed on the interpretation of the results.Comment: 18 pages, 19 figures, 2 tables, submitted to A
Visualising Mutually Non-dominating Solution Sets in Many-objective Optimisation
Copyright © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.As many-objective optimization algorithms mature, the problem owner is faced with visualizing and understanding a set of mutually nondominating solutions in a high dimensional space. We review existing methods and present new techniques to address this problem. We address a common problem with the well-known heatmap visualization, since the often arbitrary ordering of rows and columns renders the heatmap unclear, by using spectral seriation to rearrange the solutions and objectives and thus enhance the clarity of the heatmap. A multiobjective evolutionary optimizer is used to further enhance the simultaneous visualization of solutions in objective and parameter space. Two methods for visualizing multiobjective solutions in the plane are introduced. First, we use RadViz and exploit interpretations of barycentric coordinates for convex polygons and simplices to map a mutually nondominating set to the interior of a regular convex polygon in the plane, providing an intuitive representation of the solutions and objectives. Second, we introduce a new measure of the similarity of solutions—the dominance distance—which captures the order relations between solutions. This metric provides an embedding in Euclidean space, which is shown to yield coherent visualizations in two dimensions. The methods are illustrated on standard test problems and data from a benchmark many-objective problem
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