Abstract: Tungsten trioxide has been prepared by using P-PTA
as a precursor on alumina substrates by spin coating method.
Palladium introduced on WO3 film via electrolysis deposition by
using palladium chloride as catalytic precursor. The catalytic
precursor was introduced on the series of films with different
morphologies. X-ray diffractometry (XRD), Scanning electron
microscopy (SEM) and XPS were applied to analyze structure and
morphology of the fabricated thin films. Then we measured variation
of samples- electrical conductivity of pure and Pd added films in air
and diluted hydrogen. Addition of Pd resulted in a remarkable
improvement of the hydrogen sensing properties of WO3 by detection
of Hydrogen below 1% at room temperature. Also variation of the
electrical conductivity in the presence of diluted hydrogen revealed
that response of samples depends rather strongly on the palladium
configuration on the surface.
Abstract: This paper proposes a new approach to offer a private
cloud service in HPC clusters. In particular, our approach relies on
automatically scheduling users- customized environment request as a
normal job in batch system. After finishing virtualization request jobs,
those guest operating systems will dismiss so that compute nodes will
be released again for computing. We present initial work on the
innovative integration of HPC batch system and virtualization tools
that aims at coexistence such that they suffice for meeting the
minimizing interference required by a traditional HPC cluster. Given
the design of initial infrastructure, the proposed effort has the potential
to positively impact on synergy model. The results from the
experiment concluded that goal for provisioning customized cluster
environment indeed can be fulfilled by using virtual machines, and
efficiency can be improved with proper setup and arrangements.
Abstract: Interpretation of aerial images is an important task in
various applications. Image segmentation can be viewed as the essential
step for extracting information from aerial images. Among many
developed segmentation methods, the technique of clustering has been
extensively investigated and used. However, determining the number
of clusters in an image is inherently a difficult problem, especially
when a priori information on the aerial image is unavailable. This
study proposes a support vector machine approach for clustering
aerial images. Three cluster validity indices, distance-based index,
Davies-Bouldin index, and Xie-Beni index, are utilized as quantitative
measures of the quality of clustering results. Comparisons on the
effectiveness of these indices and various parameters settings on the
proposed methods are conducted. Experimental results are provided
to illustrate the feasibility of the proposed approach.
Abstract: This paper investigates the spatial structure of employment in the Jakarta Metropolitan Area (JMA), with reference to the concept of the Southeast Asian extended metropolitan region (EMR). A combination of factor analysis and local Getis-Ord (Gi*) hot-spot analysis is used to identify clusters of employment in the region, including those of the urban and agriculture sectors. Spatial statistical analysis is further used to probe the spatial association of identified employment clusters with their surroundings on several dimensions, including the spatial association between the central business district (CBD) in Jakarta city on employment density in the region, the spatial impacts of urban expansion on population growth and the degree of urban-rural interaction. The degree of spatial interaction for the whole JMA is measured by the patterns of commuting trips destined to the various employment clusters. Results reveal the strong role of the urban core of Jakarta, and the regional CBD, as the centre for mixed job sectors such as retail, wholesale, services and finance. Manufacturing and local government services, on the other hand, form corridors radiating out of the urban core, reaching out to the agriculture zones in the fringes. Strong associations between the urban expansion corridors and population growth, and urban-rural mix, are revealed particularly in the eastern and western parts of JMA. Metropolitan wide commuting patterns are focussed on the urban core of Jakarta and the CBD, while relatively local commuting patterns are shown to be prevalent for the employment corridors.
Abstract: The complex hybrid and nonlinear nature of many processes that are met in practice causes problems with both structure modelling and parameter identification; therefore, obtaining a model that is suitable for MPC is often a difficult task. The basic idea of this paper is to present an identification method for a piecewise affine (PWA) model based on a fuzzy clustering algorithm. First we introduce the PWA model. Next, we tackle the identification method. We treat the fuzzy clustering algorithm, deal with the projections of the fuzzy clusters into the input space of the PWA model and explain the estimation of the parameters of the PWA model by means of a modified least-squares method. Furthermore, we verify the usability of the proposed identification approach on a hybrid nonlinear batch reactor example. The result suggest that the batch reactor can be efficiently identified and thus formulated as a PWA model, which can eventually be used for model predictive control purposes.
Abstract: In this paper, we present a comparative study between two computer vision systems for objects recognition and tracking, these algorithms describe two different approach based on regions constituted by a set of pixels which parameterized objects in shot sequences. For the image segmentation and objects detection, the FCM technique is used, the overlapping between cluster's distribution is minimized by the use of suitable color space (other that the RGB one). The first technique takes into account a priori probabilities governing the computation of various clusters to track objects. A Parzen kernel method is described and allows identifying the players in each frame, we also show the importance of standard deviation value research of the Gaussian probability density function. Region matching is carried out by an algorithm that operates on the Mahalanobis distance between region descriptors in two subsequent frames and uses singular value decomposition to compute a set of correspondences satisfying both the principle of proximity and the principle of exclusion.
Abstract: Clustering in high dimensional space is a difficult
problem which is recurrent in many fields of science and
engineering, e.g., bioinformatics, image processing, pattern
reorganization and data mining. In high dimensional space some of
the dimensions are likely to be irrelevant, thus hiding the possible
clustering. In very high dimensions it is common for all the objects in
a dataset to be nearly equidistant from each other, completely
masking the clusters. Hence, performance of the clustering algorithm
decreases.
In this paper, we propose an algorithmic framework which
combines the (reduct) concept of rough set theory with the k-means
algorithm to remove the irrelevant dimensions in a high dimensional
space and obtain appropriate clusters. Our experiment on test data
shows that this framework increases efficiency of the clustering
process and accuracy of the results.
Abstract: In this research, the diabetes conditions of people (healthy, prediabete and diabete) were tried to be identified with noninvasive palm perspiration measurements. Data clusters gathered from 200 subjects were used (1.Individual Attributes Cluster and 2. Palm Perspiration Attributes Cluster). To decrase the dimensions of these data clusters, Principal Component Analysis Method was used. Data clusters, prepared in that way, were classified with Support Vector Machines. Classifications with highest success were 82% for Glucose parameters and 84% for HbA1c parametres.
Abstract: The present study presents a new approach to automatic
data clustering and classification problems in large and complex
databases and, at the same time, derives specific types of explicit rules
describing each cluster. The method works well in both sparse and
dense multidimensional data spaces. The members of the data space
can be of the same nature or represent different classes. A number
of N-dimensional ellipsoids are used for enclosing the data clouds.
Due to the geometry of an ellipsoid and its free rotation in space
the detection of clusters becomes very efficient. The method is based
on genetic algorithms that are used for the optimization of location,
orientation and geometric characteristics of the hyper-ellipsoids. The
proposed approach can serve as a basis for the development of
general knowledge systems for discovering hidden knowledge and
unexpected patterns and rules in various large databases.
Abstract: The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.
Abstract: The paper deals with an application of quantitative analysis – the Data Envelopment Analysis (DEA) method to performance evaluation of the European Union Member States, in the reference years 2000 and 2011. The main aim of the paper is to measure efficiency changes over the reference years and to analyze a level of productivity in individual countries based on DEA method and to classify the EU Member States to homogeneous units (clusters) according to efficiency results. The theoretical part is devoted to the fundamental basis of performance theory and the methodology of DEA. The empirical part is aimed at measuring degree of productivity and level of efficiency changes of evaluated countries by basic DEA model – CCR CRS model, and specialized DEA approach – the Malmquist Index measuring the change of technical efficiency and the movement of production possibility frontier. Here, DEA method becomes a suitable tool for setting a competitive/uncompetitive position of each country because there is not only one factor evaluated, but a set of different factors that determine the degree of economic development.
Abstract: Integration of system process information obtained
through an image processing system with an evolving knowledge
database to improve the accuracy and predictability of wear particle
analysis is the main focus of the paper. The objective is to automate
intelligently the analysis process of wear particle using classification
via self organizing maps. This is achieved using relationship
measurements among corresponding attributes of various
measurements for wear particle. Finally, visualization technique is
proposed that helps the viewer in understanding and utilizing these
relationships that enable accurate diagnostics.
Abstract: Integration of system process information obtained
through an image processing system with an evolving knowledge
database to improve the accuracy and predictability of wear debris
analysis is the main focus of the paper. The objective is to automate
intelligently the analysis process of wear particle using classification
via self-organizing maps. This is achieved using relationship
measurements among corresponding attributes of various
measurements for wear debris. Finally, visualization technique is
proposed that helps the viewer in understanding and utilizing these
relationships that enable accurate diagnostics.
Abstract: In this study a clustering technique has been implemented which is K-Means like with hierarchical initial set (HKM). The goal of this study is to prove that clustering document sets do enhancement precision on information retrieval systems, since it was proved by Bellot & El-Beze on French language. A comparison is made between the traditional information retrieval system and the clustered one. Also the effect of increasing number of clusters on precision is studied. The indexing technique is Term Frequency * Inverse Document Frequency (TF * IDF). It has been found that the effect of Hierarchical K-Means Like clustering (HKM) with 3 clusters over 242 Arabic abstract documents from the Saudi Arabian National Computer Conference has significant results compared with traditional information retrieval system without clustering. Additionally it has been found that it is not necessary to increase the number of clusters to improve precision more.