Abstract: Advances in computing applications in recent years
have prompted the demand for more flexible scheduling models for
QoS demand. Moreover, in practical applications, partly violated
temporal constraints can be tolerated if the violation meets certain
distribution. So we need extend the traditional Liu and Lanland model
to adapt to these circumstances. There are two extensions, which are
the (m, k)-firm model and Window-Constrained model. This paper
researches on weakly hard real-time constraints and their combination
to support QoS. The fact that a practical application can tolerate some
violations of temporal constraint under certain distribution is
employed to support adaptive QoS on the open real-time system. The
experiment results show these approaches are effective compared to
traditional scheduling algorithms.
Abstract: Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen's Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or character recognition, robot control and medical diagnosis, its potential as a robust substitute for clustering analysis remains relatively unresearched. SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization. In this paper, SOM is used for creating a toroidal mapping of two-dimensional lattice to perform cluster analysis on results of a chemical analysis of wines produced in the same region in Italy but derived from three different cultivators, referred to as the “wine recognition data" located in the University of California-Irvine database. The results are encouraging and it is believed that SOM would make an appealing and powerful decision-support system tool for clustering tasks and for data visualization.
Abstract: In an effort to understand the preliminary effects of aerodynamic stress on alveolar epithelial cells, we developed a multifluidic cell culture platform capable of supporting alveolar cultures at an air-liquid interface under constant air flow and exposure to varying pressure stimuli on the apical side while providing nourishment on the basolateral plane. Our current study involved utilizing the platform to study the effect of basement membrane coating and addition of dexamethasone on cellular response to pressure in A549 and H441 alveolar epithelial cells.
Abstract: The customary practice of identifying industrial sickness is a set traditional techniques which rely upon a range of manual monitoring and compilation of financial records. It makes the process tedious, time consuming and often are susceptible to manipulation. Therefore, certain readily available tools are required which can deal with such uncertain situations arising out of industrial sickness. It is more significant for a country like India where the fruits of development are rarely equally distributed. In this paper, we propose an approach based on Artificial Neural Network (ANN) to deal with industrial sickness with specific focus on a few such units taken from a less developed north-east (NE) Indian state like Assam. The proposed system provides decision regarding industrial sickness using eight different parameters which are directly related to the stages of sickness of such units. The mechanism primarily uses certain signals and symptoms of industrial health to decide upon the state of a unit. Specifically, we formulate an ANN based block with data obtained from a few selected units of Assam so that required decisions related to industrial health could be taken. The system thus formulated could become an important part of planning and development. It can also contribute towards computerization of decision support systems related to industrial health and help in better management.
Abstract: For complete support of Quality of Service, it is better that environment itself predicts resource requirements of a job by using special methods in the Grid computing. The exact and correct prediction causes exact matching of required resources with available resources. After the execution of each job, the used resources will be saved in the active database named "History". At first some of the attributes will be exploit from the main job and according to a defined similarity algorithm the most similar executed job will be exploited from "History" using statistic terms such as linear regression or average, resource requirements will be predicted. The new idea in this research is based on active database and centralized history maintenance. Implementation and testing of the proposed architecture results in accuracy percentage of 96.68% to predict CPU usage of jobs and 91.29% of memory usage and 89.80% of the band width usage.
Abstract: This study examined the underlying dimensions of
brand equity in the chocolate industry. For this purpose, researchers
developed a model to identify which factors are influential in
building brand equity. The second purpose was to assess brand
loyalty and brand images mediating effect between brand attitude,
brand personality, brand association with brand equity. The study
employed structural equation modeling to investigate the causal
relationships between the dimensions of brand equity and brand
equity itself. It specifically measured the way in which consumers’
perceptions of the dimensions of brand equity affected the overall
brand equity evaluations. Data were collected from a sample of
consumers of chocolate industry in Iran. The results of this empirical
study indicate that brand loyalty and brand image are important
components of brand equity in this industry. Moreover, the role of
brand loyalty and brand image as mediating factors in the intention of
brand equity are supported. The principal contribution of the present
research is that it provides empirical evidence of the
multidimensionality of consumer based brand equity, supporting
Aaker´s and Keller´s conceptualization of brand equity. The present
research also enriched brand equity building by incorporating the
brand personality and brand image, as recommended by previous
researchers. Moreover, creating the brand equity index in chocolate
industry of Iran particularly is novel.
Abstract: With deep development of software reuse, componentrelated
technologies have been widely applied in the development of
large-scale complex applications. Component identification (CI) is
one of the primary research problems in software reuse, by analyzing
domain business models to get a set of business components with high
reuse value and good reuse performance to support effective reuse.
Based on the concept and classification of CI, its technical stack is
briefly discussed from four views, i.e., form of input business models,
identification goals, identification strategies, and identification
process. Then various CI methods presented in literatures are
classified into four types, i.e., domain analysis based methods,
cohesion-coupling based clustering methods, CRUD matrix based
methods, and other methods, with the comparisons between these
methods for their advantages and disadvantages. Additionally, some
insufficiencies of study on CI are discussed, and the causes are
explained subsequently. Finally, it is concluded with some
significantly promising tendency about research on this problem.
Abstract: This article is an extension and a practical application
approach of Wheeler-s NEBIC theory (Net Enabled Business
Innovation Cycle). NEBIC theory is a new approach in IS research
and can be used for dynamic environment related to new technology.
Firms can follow the market changes rapidly with support of the IT
resources. Flexible firms adapt their market strategies, and respond
more quickly to customers changing behaviors. When every leading
firm in an industry has access to the same IT resources, the way that
these IT resources are managed will determine the competitive
advantages or disadvantages of firm. From Dynamic Capabilities
Perspective and from newly introduced NEBIC theory by Wheeler,
we know that only IT resources cannot deliver customer value but
good configuration of those resources can guarantee customer value
by choosing the right emerging technology, grasping the economic
opportunities through business innovation and growth. We found
evidences in literature that SOA (Service Oriented Architecture) is a
promising emerging technology which can deliver the desired
economic opportunity through modularity, flexibility and loosecoupling.
SOA can also help firms to connect in network which can
open a new window of opportunity to collaborate in innovation and
right kind of outsourcing
Abstract: In this work a new platform for mobile-health systems is
presented. System target application is providing decision support to
rescue corps or military medical personnel in combat areas. Software
architecture relies on a distributed client-server system that manages a
wireless ad-hoc networks hierarchy in which several different types of
client operate. Each client is characterized for different hardware and
software requirements. Lower hierarchy levels rely in a network of
completely custom devices that store clinical information and patient
status and are designed to form an ad-hoc network operating in the
2.4 GHz ISM band and complying with the IEEE 802.15.4 standard
(ZigBee). Medical personnel may interact with such devices, that are
called MICs (Medical Information Carriers), by means of a PDA
(Personal Digital Assistant) or a MDA (Medical Digital Assistant),
and transmit the information stored in their local databases as well as
issue a service request to the upper hierarchy levels by using IEEE
802.11 a/b/g standard (WiFi). The server acts as a repository that
stores both medical evacuation forms and associated events (e.g., a
teleconsulting request). All the actors participating in the diagnostic
or evacuation process may access asynchronously to such repository
and update its content or generate new events. The designed system
pretends to optimise and improve information spreading and flow
among all the system components with the aim of improving both
diagnostic quality and evacuation process.
Abstract: Support vector machines (SVMs) are considered to be
the best machine learning algorithms for minimizing the predictive
probability of misclassification. However, their drawback is that for
large data sets the computation of the optimal decision boundary is a
time consuming function of the size of the training set. Hence several
methods have been proposed to speed up the SVM algorithm. Here
three methods used to speed up the computation of the SVM
classifiers are compared experimentally using a musical genre
classification problem. The simplest method pre-selects a random
sample of the data before the application of the SVM algorithm. Two
additional methods use proximity graphs to pre-select data that are
near the decision boundary. One uses k-Nearest Neighbor graphs and
the other Relative Neighborhood Graphs to accomplish the task.
Abstract: The paper proposes a new concept in developing
collaborative design system. The concept framework involves
applying simulation of supply chain management to collaborative
design called – 'SCM–Based Design Tool'. The system is developed
particularly to support design activities and to integrate all facilities
together. The system is aimed to increase design productivity and
creativity. Therefore, designers and customers can collaborate by the
system since conceptual design. JAG: Jewelry Art Generator based
on artificial intelligence techniques is integrated into the system.
Moreover, the proposed system can support users as decision tool
and data propagation. The system covers since raw material supply
until product delivery. Data management and sharing information are
visually supported to designers and customers via user interface. The
system is developed on Web–assisted product development
environment. The prototype system is presented for Thai jewelry
industry as a system prototype demonstration, but applicable for
other industry.
Abstract: Steel surface defect detection is essentially one of
pattern recognition problems. Support Vector Machines (SVMs) are
known as one of the most proper classifiers in this application. In this
paper, we introduce a more accurate classification method by using
SVMs as our final classifier of the inspection system. In this scheme,
multiclass classification task is performed based on the "one-againstone"
method and different kernels are utilized for each pair of the
classes in multiclass classification of the different defects.
In the proposed system, a decision tree is employed in the first
stage for two-class classification of the steel surfaces to "defect" and
"non-defect", in order to decrease the time complexity. Based on
the experimental results, generated from over one thousand images,
the proposed multiclass classification scheme is more accurate than
the conventional methods and the overall system yields a sufficient
performance which can meet the requirements in steel manufacturing.
Abstract: The quick training algorithms and accurate solution
procedure for incremental learning aim at improving the efficiency of
training of SVR, whereas there are some disadvantages for them, i.e.
the nonconvergence of the formers for changeable training set and
the inefficiency of the latter for a massive dataset. In order to handle
the problems, a new training algorithm for a changeable training
set, named Approximation Incremental Training Algorithm (AITA),
was proposed. This paper explored the reason of nonconvergence
theoretically and discussed the realization of AITA, and finally
demonstrated the benefits of AITA both on precision and efficiency.
Abstract: We identify clawback triggers from firms- proxy
statements (Form DEF 14A) and use the likelihood of restatements to
proxy for financial reporting quality. Based on a sample of 578 U.S.
firms that voluntarily adopt clawback provisions during 2003-2009,
when restatement-based triggers could be decomposed into two types:
fraud and unintentional error, and we do observe the evidence that
using fraud triggers is associated with high financial reporting quality.
The findings support that fraud triggers can enhance deterrent effect of
clawback provision by establishing a viable disincentive against fraud,
misconduct, and otherwise harmful acts. These results are robust to
controlling for the compensation components, to different sample
specifications and to a number of sensitivity.
Abstract: Serial hierarchical support vector machine (SHSVM)
is proposed to discriminate three brain tissues which are white matter
(WM), gray matter (GM), and cerebrospinal fluid (CSF). SHSVM
has novel classification approach by repeating the hierarchical
classification on data set iteratively. It used Radial Basis Function
(rbf) Kernel with different tuning to obtain accurate results. Also as
the second approach, segmentation performed with DAGSVM
method. In this article eight univariate features from the raw DTI data
are extracted and all the possible 2D feature sets are examined within
the segmentation process. SHSVM succeed to obtain DSI values
higher than 0.95 accuracy for all the three tissues, which are higher
than DAGSVM results.
Abstract: Presents a concept for a multidisciplinary process
supporting effective task transitions between different technical
domains during the architectural design stage.
A system configuration challenge is the multifunctional driven
increased solution space. As a consequence, more iteration is needed
to find a global optimum, i.e. a compromise between involved
disciplines without negative impact on development time. Since state
of the art standards like ISO 15288 and VDI 2206 do not provide a
detailed methodology on multidisciplinary design process, higher
uncertainties regarding final specifications arise. This leads to the
need of more detailed and standardized concepts or processes which
could mitigate risks.
The performed work is based on analysis of multidisciplinary
interaction, of modeling and simulation techniques. To demonstrate
and prove the applicability of the presented concept, it is applied to
the design of aircraft high lift systems, in the context of the
engineering disciplines kinematics, actuation, monitoring, installation
and structure design.
Abstract: New generation mobile communication networks have
the ability of supporting triple play. In order that, Orthogonal
Frequency Division Multiplexing (OFDM) access techniques have
been chosen to enlarge the system ability for high data rates
networks. Many of cross-layer modeling and optimization schemes
for Quality of Service (QoS) and capacity of downlink multiuser
OFDM system were proposed. In this paper, the Maximum Weighted
Capacity (MWC) based resource allocation at the Physical (PHY)
layer is used. This resource allocation scheme provides a much better
QoS than the previous resource allocation schemes, while
maintaining the highest or nearly highest capacity and costing similar
complexity. In addition, the Delay Satisfaction (DS) scheduling at the
Medium Access Control (MAC) layer, which allows more than one
connection to be served in each slot is used. This scheduling
technique is more efficient than conventional scheduling to
investigate both of the number of users as well as the number of
subcarriers against system capacity. The system will be optimized for
different operational environments: the outdoor deployment scenarios
as well as the indoor deployment scenarios are investigated and also
for different channel models. In addition, effective capacity approach
[1] is used not only for providing QoS for different mobile users, but
also to increase the total wireless network's throughput.
Abstract: Heterogeneity has to be taken into account when
integrating a set of existing information sources into a distributed
information system that are nowadays often based on Service-
Oriented Architectures (SOA). This is also particularly applicable to
distributed services such as event monitoring, which are useful in the
context of Event Driven Architectures (EDA) and Complex Event
Processing (CEP). Web services deal with this heterogeneity at a
technical level, also providing little support for event processing. Our
central thesis is that such a fully generic solution cannot provide
complete support for event monitoring; instead, source specific
semantics such as certain event types or support for certain event
monitoring techniques have to be taken into account. Our core result
is the design of a configurable event monitoring (Web) service that
allows us to trade genericity for the exploitation of source specific
characteristics. It thus delivers results for the areas of SOA, Web
services, CEP and EDA.
Abstract: Genetic Folding (GF) a new class of EA named as is
introduced for the first time. It is based on chromosomes composed
of floating genes structurally organized in a parent form and
separated by dots. Although, the genotype/phenotype system of GF
generates a kernel expression, which is the objective function of
superior classifier. In this work the question of the satisfying
mapping-s rules in evolving populations is addressed by analyzing
populations undergoing either Mercer-s or none Mercer-s rule. The
results presented here show that populations undergoing Mercer-s
rules improve practically models selection of Support Vector
Machine (SVM). The experiment is trained multi-classification
problem and tested on nonlinear Ionosphere dataset. The target of this
paper is to answer the question of evolving Mercer-s rule in SVM
addressed using either genetic folding satisfied kernel-s rules or not
applied to complicated domains and problems.
Abstract: In this paper processes including large deformations of a rubber with hyperelastic material behavior are simulated by the RKPM method. Due to the loss of kronecker delta properties in the mesh less shape functions, the imposition of essential boundary conditions consumes significant CPU time in mesh free computations. In this work transformation method is used for imposition of essential boundary conditions. A RKPM material shape function is used in this analysis. The support of the material shape functions covers the same set of particles during material deformation and hence the transformation matrix is formed only once at the initial stages. A computer program in MATLAB is developed for simulations.