Abstract: A business case is a proposal for an investment
initiative to satisfy business and functional requirements. The
business case provides the foundation for tactical decision making
and technology risk management. It helps to clarify how the
organization will use its resources in the best way by providing
justification for investment of resources. This paper describes how
simulation was used for business case benefits and return on
investment for the procurement of 8 production machines. With
investment costs of about 4.7 million dollars and annual operating
costs of about 1.3 million, we needed to determine if the machines
would provide enough cost savings and cost avoidance. We
constructed a model of the existing factory environment consisting of
8 machines and subsequently, we conducted average day simulations
with light and heavy volumes to facilitate planning decisions
required to be documented and substantiated in the business case.
Abstract: The effect of time-periodic oscillations of the Rayleigh- Benard system on the heat transport in dielectric liquids is investigated by weakly nonlinear analysis. We focus on stationary convection using the slow time scale and arrive at the real Ginzburg- Landau equation. Classical fourth order Runge-kutta method is used to solve the Ginzburg-Landau equation which gives the amplitude of convection and this helps in quantifying the heat transfer in dielectric liquids in terms of the Nusselt number. The effect of electrical Rayleigh number and the amplitude of modulation on heat transport is studied.
Abstract: The objective of this paper is to support the application of Open Innovation practices in firms and organizations by the assessment and management of Intellectual Capital. Intellectual Capital constituents are analyzed in order to verify their capability of acting as key drivers of Open Innovation processes and, therefore, of creating value. A methodology is defined to settle a procedure which helps to select the most relevant Intellectual Capital value drivers and to provide Communities of Innovation with strategic and managerial guidelines in sustaining Open Innovation paradigm. An application of the methodology is developed within a specifically addressed project and its results are hereafter examined.
Abstract: Sequences of execution of algorithms in an interactive
manner using multimedia tools are employed in this paper. It helps to
realize the concept of fundamentals of algorithms such as searching
and sorting method in a simple manner. Visualization gains more
attention than theoretical study and it is an easy way of learning
process. We propose methods for finding runtime sequence of each
algorithm in an interactive way and aims to overcome the drawbacks
of the existing character systems. System illustrates each and every
step clearly using text and animation. Comparisons of its time
complexity have been carried out and results show that our approach
provides better perceptive of algorithms.
Abstract: Many Wireless Sensor Network (WSN) applications necessitate secure multicast services for the purpose of broadcasting delay sensitive data like video files and live telecast at fixed time-slot. This work provides a novel method to deal with end-to-end delay and drop rate of packets. Opportunistic Routing chooses a link based on the maximum probability of packet delivery ratio. Null Key Generation helps in authenticating packets to the receiver. Markov Decision Process based Adaptive Scheduling algorithm determines the time slot for packet transmission. Both theoretical analysis and simulation results show that the proposed protocol ensures better performance in terms of packet delivery ratio, average end-to-end delay and normalized routing overhead.
Abstract: The fault detection and diagnosis of complicated
production processes is one of essential tasks needed to run the process
safely with good final product quality. Unexpected events occurred in
the process may have a serious impact on the process. In this work,
triangular representation of process measurement data obtained in an
on-line basis is evaluated using simulation process. The effect of using
linear and nonlinear reduced spaces is also tested. Their diagnosis
performance was demonstrated using multivariate fault data. It has
shown that the nonlinear technique based diagnosis method produced
more reliable results and outperforms linear method. The use of
appropriate reduced space yielded better diagnosis performance. The
presented diagnosis framework is different from existing ones in that it
attempts to extract the fault pattern in the reduced space, not in the
original process variable space. The use of reduced model space helps
to mitigate the sensitivity of the fault pattern to noise.
Abstract: This paper develops an unscented grid-based filter
and a smoother for accurate nonlinear modeling and analysis
of time series. The filter uses unscented deterministic sampling
during both the time and measurement updating phases, to approximate
directly the distributions of the latent state variable. A
complementary grid smoother is also made to enable computing
of the likelihood. This helps us to formulate an expectation
maximisation algorithm for maximum likelihood estimation of
the state noise and the observation noise. Empirical investigations
show that the proposed unscented grid filter/smoother compares
favourably to other similar filters on nonlinear estimation tasks.
Abstract: In this paper, a pipelined version of genetic algorithm,
called PLGA, and a corresponding hardware platform are described.
The basic operations of conventional GA (CGA) are made pipelined
using an appropriate selection scheme. The selection operator, used
here, is stochastic in nature and is called SA-selection. This helps
maintaining the basic generational nature of the proposed pipelined
GA (PLGA). A number of benchmark problems are used to compare
the performances of conventional roulette-wheel selection and the
SA-selection. These include unimodal and multimodal functions with
dimensionality varying from very small to very large. It is seen that
the SA-selection scheme is giving comparable performances with
respect to the classical roulette-wheel selection scheme, for all the
instances, when quality of solutions and rate of convergence are considered.
The speedups obtained by PLGA for different benchmarks
are found to be significant. It is shown that a complete hardware
pipeline can be developed using the proposed scheme, if parallel
evaluation of the fitness expression is possible. In this connection
a low-cost but very fast hardware evaluation unit is described.
Results of simulation experiments show that in a pipelined hardware
environment, PLGA will be much faster than CGA. In terms of
efficiency, PLGA is found to outperform parallel GA (PGA) also.
Abstract: This paper focuses on analyzing medical diagnostic data using classification rules in data mining and context reduction in formal concept analysis. It helps in finding redundancies among the various medical examination tests used in diagnosis of a disease. Classification rules have been derived from positive and negative association rules using the Concept lattice structure of the Formal Concept Analysis. Context reduction technique given in Formal Concept Analysis along with classification rules has been used to find redundancies among the various medical examination tests. Also it finds out whether expensive medical tests can be replaced by some cheaper tests.
Abstract: Accurate timing alignment and stability is important
to maximize the true counts and minimize the random counts in
positron emission tomography So signals output from detectors must
be centering with the two isotopes to pre-operation and fed signals
into four units of pulse-processing units, each unit can accept up to
eight inputs. The dual source computed tomography consist two units
on the left for 15 detector signals of Cs-137 isotope and two units on
the right are for 15 detectors signals of Co-60 isotope. The gamma
spectrum consisting of either single or multiple photo peaks. This
allows for the use of energy discrimination electronic hardware
associated with the data acquisition system to acquire photon counts
data with a specific energy, even if poor energy resolution detectors
are used. This also helps to avoid counting of the Compton scatter
counts especially if a single discrete gamma photo peak is emitted by
the source as in the case of Cs-137. In this study the polyenergetic
version of the alternating minimization algorithm is applied to the
dual energy gamma computed tomography problem.
Abstract: Lossless compression schemes with secure
transmission play a key role in telemedicine applications that helps in
accurate diagnosis and research. Traditional cryptographic algorithms
for data security are not fast enough to process vast amount of data.
Hence a novel Secured lossless compression approach proposed in
this paper is based on reversible integer wavelet transform, EZW
algorithm, new modified runlength coding for character
representation and selective bit scrambling. The use of the lifting
scheme allows generating truly lossless integer-to-integer wavelet
transforms. Images are compressed/decompressed by well-known
EZW algorithm. The proposed modified runlength coding greatly
improves the compression performance and also increases the
security level. This work employs scrambling method which is fast,
simple to implement and it provides security. Lossless compression
ratios and distortion performance of this proposed method are found
to be better than other lossless techniques.
Abstract: The accelerated growth in aircraft industries desire
effectual schemes, programs, innovative designs of advanced systems
and facilities to accomplish the augmenting need for home-free air
transportation. In this paper, a contemporary conceptual design of a
cambered airfoil has been proposed in order to providing augmented
effective lift force relative to the airplane, and to eliminating
drawbacks and limitations of an airfoil in a commercial airplane by
using a kind of smart materials. This invention of an unsymmetrical
airfoil structure utilizes the amplified air momentum around the
airfoil and increased camber length to providing improved aircraft
performance and assist to enhancing the reliability of the aircraft
components. Moreover, this conjectured design helps to reducing
airplane weight and total drag.
Abstract: Developing techniques for mobile robot navigation constitutes one of the major trends in the current
research on mobile robotics. This paper develops a local
model network (LMN) for mobile robot navigation. The
LMN represents the mobile robot by a set of locally valid
submodels that are Multi-Layer Perceptrons (MLPs).
Training these submodels employs Back Propagation (BP) algorithm. The paper proposes the fuzzy C-means (FCM) in this scheme to divide the input space to sub regions, and then a submodel (MLP) is identified to represent a particular
region. The submodels then are combined in a unified
structure. In run time phase, Radial Basis Functions (RBFs) are employed as windows for the activated submodels. This
proposed structure overcomes the problem of changing operating regions of mobile robots. Read data are used in all experiments. Results for mobile robot navigation using the
proposed LMN reflect the soundness of the proposed
scheme.
Abstract: In this study, a Loop Back Algorithm for component
connected labeling for detecting objects in a digital image is
presented. The approach is using loop back connected component
labeling algorithm that helps the system to distinguish the object
detected according to their label. Deferent than whole window
scanning technique, this technique reduces the searching time for
locating the object by focusing on the suspected object based on
certain features defined. In this study, the approach was also
implemented for a face detection system. Face detection system is
becoming interesting research since there are many devices or
systems that require detecting the face for certain purposes. The input
can be from still image or videos, therefore the sub process of this
system has to be simple, efficient and accurate to give a good result.
Abstract: Data mining uses a variety of techniques each of which
is useful for some particular task. It is important to have a deep
understanding of each technique and be able to perform sophisticated
analysis. In this article we describe a tool built to simulate a variation
of the Kohonen network to perform unsupervised clustering and
support the entire data mining process up to results visualization. A
graphical representation helps the user to find out a strategy to
optimize classification by adding, moving or delete a neuron in order
to change the number of classes. The tool is able to automatically
suggest a strategy to optimize the number of classes optimization, but
also support both tree classifications and semi-lattice organizations of
the classes to give to the users the possibility of passing from one
class to the ones with which it has some aspects in common.
Examples of using tree and semi-lattice classifications are given to
illustrate advantages and problems. The tool is applied to classify
macroeconomic data that report the most developed countries- import
and export. It is possible to classify the countries based on their
economic behaviour and use the tool to characterize the commercial
behaviour of a country in a selected class from the analysis of
positive and negative features that contribute to classes formation.
Possible interrelationships between the classes and their meaning are
also discussed.
Abstract: In practice, wireless networks has the property that
the signal strength attenuates with respect to the distance from the
base station, it could be better if the nodes at two hop away are
considered for better quality of service. In this paper, we propose a
procedure to identify delay preserving substructures for a given
wireless ad-hoc network using a new graph operation G 2 – E (G) =
G* (Edge difference of square graph of a given graph and the
original graph). This operation helps to analyze some induced
substructures, which preserve delay in communication among them.
This operation G* on a given graph will induce a graph, in which 1-
hop neighbors of any node are at 2-hop distance in the original
network. In this paper, we also identify some delay preserving
substructures in G*, which are (i) set of all nodes, which are mutually
at 2-hop distance in G that will form a clique in G*, (ii) set of nodes
which forms an odd cycle C2k+1 in G, will form an odd cycle in G*
and the set of nodes which form a even cycle C2k in G that will form
two disjoint companion cycles ( of same parity odd/even) of length k
in G*, (iii) every path of length 2k+1 or 2k in G will induce two
disjoint paths of length k in G*, and (iv) set of nodes in G*, which
induces a maximal connected sub graph with radius 1 (which
identifies a substructure with radius equal 2 and diameter at most 4 in
G). The above delay preserving sub structures will behave as good
clusters in the original network.
Abstract: Despite various methods that exist in software risk management, software projects have a high rate of failure. When complexity and size of the projects are increased, managing software development becomes more difficult. In these projects the need for more analysis and risk assessment is vital. In this paper, a classification for software risks is specified. Then relations between these risks using risk tree structure are presented. Analysis and assessment of these risks are done using probabilistic calculations. This analysis helps qualitative and quantitative assessment of risk of failure. Moreover it can help software risk management process. This classification and risk tree structure can apply to some software tools.
Abstract: Security has been an important issue and concern in the
smart home systems. Smart home networks consist of a wide range of
wired or wireless devices, there is possibility that illegal access to
some restricted data or devices may happen. Password-based
authentication is widely used to identify authorize users, because this
method is cheap, easy and quite accurate. In this paper, a neural
network is trained to store the passwords instead of using verification
table. This method is useful in solving security problems that
happened in some authentication system. The conventional way to
train the network using Backpropagation (BPN) requires a long
training time. Hence, a faster training algorithm, Resilient
Backpropagation (RPROP) is embedded to the MLPs Neural
Network to accelerate the training process. For the Data Part, 200
sets of UserID and Passwords were created and encoded into binary
as the input. The simulation had been carried out to evaluate the
performance for different number of hidden neurons and combination
of transfer functions. Mean Square Error (MSE), training time and
number of epochs are used to determine the network performance.
From the results obtained, using Tansig and Purelin in hidden and
output layer and 250 hidden neurons gave the better performance. As
a result, a password-based user authentication system for smart home
by using neural network had been developed successfully.
Abstract: Wavelet neural networks (WNNs) have emerged as a vital alternative to the vastly studied multilayer perceptrons (MLPs) since its first implementation. In this paper, we applied various clustering algorithms, namely, K-means (KM), Fuzzy C-means (FCM), symmetry-based K-means (SBKM), symmetry-based Fuzzy C-means (SBFCM) and modified point symmetry-based K-means (MPKM) clustering algorithms in choosing the translation parameter of a WNN. These modified WNNs are further applied to the heterogeneous cancer classification using benchmark microarray data and were compared against the conventional WNN with random initialization method. Experimental results showed that a WNN classifier with the MPKM algorithm is more precise than the conventional WNN as well as the WNNs with other clustering algorithms.
Abstract: In elevating performance in competetive sports, an
athlete must continously train in achieving maximum
performance,but needs to pay attention to recovery therapy, that is to
recover from fatigue as well as injury.The correct recovery therapy
will assist in process of recovery and helps in the training in
achieving better performace. Binahong (Anredera cordifolia) was
proven empirically by the locals in assisting speedy recovery from an
injury.Clinical research with lab animals receiving blunt trauma
injury, microscopically shown signs of: 1) redness, 2) heatiness, 3)
swelling and, 4) lack of activity. There is also microscopic indication
of: 1) infiltration of inflame cells (migration of cells to the trauma
area), 2) Cells necrosis, 3) Congestion (as a result of dead red blood
cells), 4) uedema. On administration of Binahong for 3 days, there is
a significant drop of 5% in cell inflammation, 2% increase of
fibroblast (cell membrance) count.Conclutin: Binahong do assist in
reducing cell inflammation and increase counts of cells fibroblast.
Suggestion: In helping athlete's to recover from force injury, we need
study about Binahong's roots to inflammation cell and healing of
injuried cell.