Abstract: This paper describes an experience of research,
development and innovation applied in Industrial Naval at (Science
and Technology Corporation for the Development of Shipbuilding
Industry, Naval in Colombia (COTECMAR) particularly through
processes of research, innovation and technological development,
based on theoretical models related to organizational knowledge
management, technology management and management of human
talent and integration of technology platforms. It seeks ways to
facilitate the initial establishment of environments rich in
information, knowledge and content-supported collaborative
strategies on dynamic processes missionary, seeking further
development in the context of research, development and innovation
of the Naval Engineering in Colombia, making it a distinct basis for
the generation of knowledge assets from COTECMAR.
The integration of information and communication technologies,
supported on emerging technologies (mobile technologies, wireless,
digital content via PDA, and content delivery services on the Web 2.0
and Web 3.0) as a view of the strategic thrusts in any organization
facilitates the redefinition of processes for managing information and
knowledge, enabling the redesign of workflows, the adaptation of
new forms of organization - preferably in networking and support the
creation of symbolic-inside-knowledge promotes the development of
new skills, knowledge and attitudes of the knowledge worker
Abstract: The Requirements Abstraction Model (RAM) helps in managing abstraction in requirements by organizing them at four levels (product, feature, function and component). The RAM is adaptable and can be tailored to meet the needs of the various organizations. Because software requirements are an important source of information for developing high-level tests, organizations willing to adopt the RAM model need to know the suitability of the RAM requirements for developing high-level tests. To investigate this suitability, test cases from twenty randomly selected requirements were developed, analyzed and graded. Requirements were selected from the requirements document of a Course Management System, a web based software system that supports teachers and students in performing course related tasks. This paper describes the results of the requirements document analysis. The results show that requirements at lower levels in the RAM are suitable for developing executable tests whereas it is hard to develop from requirements at higher levels.
Abstract: This research studied about green logistics and the
expected benefit that organization gotten when adapted to green
logistics also the organization concerned about the important activity
in green logistics to apply in implementation from study was found
that the benefit of green logistics that organization was gotten by
logistics management which was the increased efficiency process of
management the product from producer to customer all of reduce
production cost, increased value added save energy and prevented
environment together
From study was found that the organization had green logistics to
apply in logistics activities in supply chain since downstream till
upstream to prevent environment as follow 1). Purchasing process,
trade facilitation enhance such as linking of information technology
during business to business (B2B business). 2). Productions process
improved by business logistics improvement 3). Warehouse
management process such as recycled packaging, moving goods in to
warehouse, transportation goods and inside receiving and delivery
products plan.
Abstract: In this paper, we proposed a novel receiver algorithm
for coherent underwater acoustic communications. The proposed
receiver is composed of three parts: (1) Doppler tracking and
correction, (2) Time reversal channel estimation and combining, and
(3) Joint iterative equalization and decoding (JIED). To reduce
computational complexity and optimize the equalization algorithm,
Time reversal (TR) channel estimation and combining is adopted to
simplify multi-channel adaptive decision feedback equalizer (ADFE)
into single channel ADFE without reducing the system performance.
Simultaneously, the turbo theory is adopted to form joint iterative
ADFE and convolutional decoder (JIED). In JIED scheme, the ADFE
and decoder exchange soft information in an iterative manner, which
can enhance the equalizer performance using decoding gain. The
simulation results show that the proposed algorithm can reduce
computational complexity and improve the performance of equalizer.
Therefore, the performance of coherent underwater acoustic
communications can be improved greatly.
Abstract: In this paper, a mathematical model for data object replication in ad hoc networks is formulated. The derived model is general, flexible and adaptable to cater for various applications in ad hoc networks. We propose a game theoretical technique in which players (mobile hosts) continuously compete in a non-cooperative environment to improve data accessibility by replicating data objects. The technique incorporates the access frequency from mobile hosts to each data object, the status of the network connectivity, and communication costs. The proposed technique is extensively evaluated against four well-known ad hoc network replica allocation methods. The experimental results reveal that the proposed approach outperforms the four techniques in both the execution time and solution quality
Abstract: Adaptive observers used in sensorless control of induction motors suffer from instability especally in regenerating mode. In this paper, an optimal feed back gain design is proposed, it can reduce the instability region in the torque speed plane .
Abstract: Biologically human brain processes information in both unimodal and multimodal approaches. In fact, information is progressively abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has exponentially produced various sources of data, which could be likened to being the state of multimodality in human brain. Therefore, this is an inspiration to develop a methodology for exploring multimodal data and further identifying multi-view patterns. Specifically, we propose a brain inspired conceptual model that allows exploration and identification of patterns at different levels of granularity, different types of hierarchies and different types of modalities. A structurally adaptive neural network is deployed to implement the proposed model. Furthermore, the acquisition of multi-view patterns with the proposed model is demonstrated and discussed with some experimental results.
Abstract: Magneto-rheological (MR) fluid damper is a semiactive
control device that has recently received more attention by the
vibration control community. But inherent hysteretic and highly
nonlinear dynamics of MR fluid damper is one of the challenging
aspects to employ its unique characteristics. The combination of
artificial neural network (ANN) and fuzzy logic system (FLS) have
been used to imitate more precisely the behavior of this device.
However, the derivative-based nature of adaptive networks causes
some deficiencies. Therefore, in this paper, a novel approach that
employ genetic algorithm, as a free-derivative algorithm, to enhance
the capability of fuzzy systems, is proposed. The proposed method
used to model MR damper. The results will be compared with
adaptive neuro-fuzzy inference system (ANFIS) model, which is one
of the well-known approaches in soft computing framework, and two
best parametric models of MR damper. Data are generated based on
benchmark program by applying a number of famous earthquake
records.
Abstract: Clusters of microcalcifications in mammograms are an
important sign of breast cancer. This paper presents a complete
Computer Aided Detection (CAD) scheme for automatic detection of
clustered microcalcifications in digital mammograms. The proposed
system, MammoScan μCaD, consists of three main steps. Firstly
all potential microcalcifications are detected using a a method for
feature extraction, VarMet, and adaptive thresholding. This will also
give a number of false detections. The goal of the second step,
Classifier level 1, is to remove everything but microcalcifications.
The last step, Classifier level 2, uses learned dictionaries and sparse
representations as a texture classification technique to distinguish
single, benign microcalcifications from clustered microcalcifications,
in addition to remove some remaining false detections. The system
is trained and tested on true digital data from Stavanger University
Hospital, and the results are evaluated by radiologists. The overall
results are promising, with a sensitivity > 90 % and a low false
detection rate (approx 1 unwanted pr. image, or 0.3 false pr. image).
Abstract: In this paper, we are going to determine the threshold levels of adaptive modulation in a burst by burst CDMA system by a suboptimum method so that the above method attempts to increase the average bit per symbol (BPS) rate of transceiver system by switching between the different modulation modes in variable channel condition. In this method, we choose the minimum values of average bit error rate (BER) and maximum values of average BPS on different values of average channel signal to noise ratio (SNR) and then calculate the relative threshold levels of them, so that when the instantaneous SNR increases, a higher order modulation be employed for increasing throughput and vise-versa when the instantaneous SNR decreases, a lower order modulation be employed for improvement of BER. In transmission step, by this adaptive modulation method, in according to comparison between obtained estimation of pilot symbols and a set of above suboptimum threshold levels, above system chooses one of states no transmission, BPSK, 4QAM and square 16QAM for modulation of data. The expected channel in this paper is a slow Rayleigh fading.
Abstract: Our adaptive multimodal system aims at correctly
presenting a mathematical expression to visually impaired users.
Given an interaction context (i.e. combination of user, environment
and system resources) as well as the complexity of the expression
itself and the user-s preferences, the suitability scores of different
presentation format are calculated. Unlike the current state-of-the art
solutions, our approach takes into account the user-s situation and not
imposes a solution that is not suitable to his context and capacity. In
this wok, we present our methodology for calculating the
mathematical expression complexity and the results of our
experiment. Finally, this paper discusses the concepts and principles
applied on our system as well as their validation through cases
studies. This work is our original contribution to an ongoing research
to make informatics more accessible to handicapped users.
Abstract: In this paper the behavior of the decision feedback
equalizers (DFEs) adapted by the decision-directed or the constant
modulus blind algorithms is presented. An analysis of the error
surface of the corresponding criterion cost functions is first
developed. With the intention of avoiding the ill-convergence of the
algorithm, the paper proposes to modify the shape of the cost
function error surface by using a soft decision instead of the hard
one. This was shown to reduce the influence of false decisions and to
smooth the undesirable minima. Modified algorithms using the soft
decision during a pseudo-training phase with an automatic switch to
the properly tracking phase are then derived. Computer simulations
show that these modified algorithms present better ability to avoid
local minima than conventional ones.
Abstract: An influence of intranasal combined injection of live
cold-adapted influenza vaccine with chitosan derivatives as adjuvants
on the subpopulation structure of mononuclear leukocytes of mouse
spleen which reflects the orientation of the immune response was
studied. It is found that the inclusion of chitosan preparations
promotes activation of cellular-level of immune response.
Abstract: In Data mining, Fuzzy clustering algorithms have
demonstrated advantage over crisp clustering algorithms in dealing
with the challenges posed by large collections of vague and uncertain
natural data. This paper reviews concept of fuzzy logic and fuzzy
clustering. The classical fuzzy c-means algorithm is presented and its
limitations are highlighted. Based on the study of the fuzzy c-means
algorithm and its extensions, we propose a modification to the cmeans
algorithm to overcome the limitations of it in calculating the
new cluster centers and in finding the membership values with
natural data. The efficiency of the new modified method is
demonstrated on real data collected for Bhutan-s Gross National
Happiness (GNH) program.
Abstract: Segmentation of Magnetic Resonance Imaging (MRI) images is the most challenging problems in medical imaging. This paper compares the performances of Seed-Based Region Growing (SBRG), Adaptive Network-Based Fuzzy Inference System (ANFIS) and Fuzzy c-Means (FCM) in brain abnormalities segmentation. Controlled experimental data is used, which designed in such a way that prior knowledge of the size of the abnormalities are known. This is done by cutting various sizes of abnormalities and pasting it onto normal brain tissues. The normal tissues or the background are divided into three different categories. The segmentation is done with fifty seven data of each category. The knowledge of the size of the abnormalities by the number of pixels are then compared with segmentation results of three techniques proposed. It was proven that the ANFIS returns the best segmentation performances in light abnormalities, whereas the SBRG on the other hand performed well in dark abnormalities segmentation.
Abstract: The overall objective of this paper is to retrieve soil
surfaces parameters namely, roughness and soil moisture related to
the dielectric constant by inverting the radar backscattered signal
from natural soil surfaces.
Because the classical description of roughness using statistical
parameters like the correlation length doesn't lead to satisfactory
results to predict radar backscattering, we used a multi-scale
roughness description using the wavelet transform and the Mallat
algorithm. In this description, the surface is considered as a
superposition of a finite number of one-dimensional Gaussian
processes each having a spatial scale. A second step in this study
consisted in adapting a direct model simulating radar backscattering
namely the small perturbation model to this multi-scale surface
description. We investigated the impact of this description on radar
backscattering through a sensitivity analysis of backscattering
coefficient to the multi-scale roughness parameters.
To perform the inversion of the small perturbation multi-scale
scattering model (MLS SPM) we used a multi-layer neural network
architecture trained by backpropagation learning rule. The inversion
leads to satisfactory results with a relative uncertainty of 8%.
Abstract: This article presents the development of a neural
network cognitive model for the classification and detection of
different frequency signals. The basic structure of the implemented
neural network was inspired on the perception process that humans
generally make in order to visually distinguish between high and low
frequency signals. It is based on the dynamic neural network concept,
with delays. A special two-layer feedforward neural net structure was
successfully implemented, trained and validated, to achieve
minimum target error. Training confirmed that this neural net
structure descents and converges to a human perception classification
solution, even when far away from the target.
Abstract: In order to provide accurate heart rate variability
indices of sympathetic and parasympathetic activity, the low
frequency and high frequency components of an RR heart rate signal
must be adequately separated. This is not always possible by just
applying spectral analysis, as power from the high and low frequency
components often leak into their adjacent bands. Furthermore,
without the respiratory spectra it is not obvious that the low
frequency component is not another respiratory component, which
can appear in the lower band. This paper describes an adaptive filter,
which aids the separation of the low frequency sympathetic and high
frequency parasympathetic components from an ECG R-R interval
signal, enabling the attainment of more accurate heart rate variability
measures. The algorithm is applied to simulated signals and heart rate
and respiratory signals acquired from an ambulatory monitor
incorporating single lead ECG and inductive plethysmography
sensors embedded in a garment. The results show an improvement
over standard heart rate variability spectral measurements.
Abstract: Most integrated inertial navigation systems (INS) and
global positioning systems (GPS) have been implemented using the
Kalman filtering technique with its drawbacks related to the need for
predefined INS error model and observability of at least four
satellites. Most recently, a method using a hybrid-adaptive network
based fuzzy inference system (ANFIS) has been proposed which is
trained during the availability of GPS signal to map the error
between the GPS and the INS. Then it will be used to predict the
error of the INS position components during GPS signal blockage.
This paper introduces a genetic optimization algorithm that is used to
update the ANFIS parameters with respect to the INS/GPS error
function used as the objective function to be minimized. The results
demonstrate the advantages of the genetically optimized ANFIS for
INS/GPS integration in comparison with conventional ANFIS
specially in the cases of satellites- outages. Coping with this problem
plays an important role in assessment of the fusion approach in land
navigation.
Abstract: In this paper, we study the multi-scenario knapsack problem, a variant of the well-known NP-Hard single knapsack problem. We investigate the use of an adaptive algorithm for solving heuristically the problem. The used method combines two complementary phases: a size reduction phase and a dynamic 2- opt procedure one. First, the reduction phase applies a polynomial reduction strategy; that is used for reducing the size problem. Second, the adaptive search procedure is applied in order to attain a feasible solution Finally, the performances of two versions of the proposed algorithm are evaluated on a set of randomly generated instances.