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 an algorithm based on the adaptive
neuro-fuzzy controller is provided to enhance the tipover stability of
mobile manipulators when they are subjected to predefined
trajectories for the end-effector and the vehicle. The controller
creates proper configurations for the manipulator to prevent the robot
from being overturned. The optimal configuration and thus the most
favorable control are obtained through soft computing approaches
including a combination of genetic algorithm, neural networks, and
fuzzy logic. The proposed algorithm, in this paper, is that a look-up
table is designed by employing the obtained values from the genetic
algorithm in order to minimize the performance index and by using
this data base, rule bases are designed for the ANFIS controller and
will be exerted on the actuators to enhance the tipover stability of the
mobile manipulator. A numerical example is presented to
demonstrate the effectiveness of the proposed algorithm.
Abstract: Automatic reusability appraisal could be helpful in
evaluating the quality of developed or developing reusable software
components and in identification of reusable components from
existing legacy systems; that can save cost of developing the software
from scratch. But the issue of how to identify reusable components
from existing systems has remained relatively unexplored. In this
paper, we have mentioned two-tier approach by studying the
structural attributes as well as usability or relevancy of the
component to a particular domain. Latent semantic analysis is used
for the feature vector representation of various software domains. It
exploits the fact that FeatureVector codes can be seen as documents
containing terms -the idenifiers present in the components- and so
text modeling methods that capture co-occurrence information in
low-dimensional spaces can be used. Further, we devised Neuro-
Fuzzy hybrid Inference System, which takes structural metric values
as input and calculates the reusability of the software component.
Decision tree algorithm is used to decide initial set of fuzzy rules for
the Neuro-fuzzy system. The results obtained are convincing enough
to propose the system for economical identification and retrieval of
reusable software components.
Abstract: A Novel fuzzy neural network combining with support vector learning mechanism called support-vector-based fuzzy neural networks (SVBFNN) is proposed. The SVBFNN combine the capability of minimizing the empirical risk (training error) and expected risk (testing error) of support vector learning in high dimensional data spaces and the efficient human-like reasoning of FNN.
Abstract: Coloured Petri net (CPN) has been widely adopted in various areas in Computer Science, including protocol specification, performance evaluation, distributed systems and coordination in multi-agent systems. It provides a graphical representation of a system and has a strong mathematical foundation for proving various properties. This paper proposes a novel representation of a coloured Petri net using an extension of logic programming called abductive logic programming (ALP), which is purely based on classical logic. Under such a representation, an implementation of a CPN could be directly obtained, in which every inference step could be treated as a kind of equivalence preserved transformation. We would describe how to implement a CPN under such a representation using common meta-programming techniques in Prolog. We call our framework CPN-LP and illustrate its applications in modeling an intelligent agent.
Abstract: A logic model for analyzing complex systems- stability
is very useful to many areas of sciences. In the real world, we are
enlightened from some natural phenomena such as “biosphere", “food
chain", “ecological balance" etc. By research and practice, and taking
advantage of the orthogonality and symmetry defined by the theory of
multilateral matrices, we put forward a logic analysis model of
stability of complex systems with three relations, and prove it by
means of mathematics. This logic model is usually successful in
analyzing stability of a complex system. The structure of the logic
model is not only clear and simple, but also can be easily used to
research and solve many stability problems of complex systems. As an
application, some examples are given.
Abstract: This paper presents the development and application of an adaptive neuro fuzzy inference system (ANFIS) based intelligent hybrid neuro fuzzy controller for automatic generation control (AGC) of two-area interconnected thermal power system with reheat non linearity. The dynamic response of the system has been studied for 1% step load perturbation in area-1. The performance of the proposed neuro fuzzy controller is compared against conventional proportional-integral (PI) controller, state feedback linear quadratic regulator (LQR) controller and fuzzy gain scheduled proportionalintegral (FGSPI) controller. Comparative analysis demonstrates that the proposed intelligent neuro fuzzy controller is the most effective of all in improving the transients of frequency and tie-line power deviations against small step load disturbances. Simulations have been performed using Matlab®.
Abstract: The main purpose of the study was to determine whether students- interpretation achievement differed with the use of various multimedia presentation types. Four groups of students, text only (T), audio only (A), text and audio (TA), text and image (TI), were arranged and they were presented the same story via different types of multimedia presentations. Inference achievement was measured by a critical thinking inference test. Higher mean scores for the TA group compared to the other three groups were found. Also when compared pairwise, interpretation achievement of the TA group differed significantly from scores of the T and TI groups. These differences were interpreted with the increased cognitive load. Increased cognitive load for the TA group may have invited students to put more effort into comprehending the text, thus resulting in better test scores. Findings of the study can be seen as a sign of the importance of learning situations and learning outcomes in multimedia-supported learning environments and may have practical benefits for instructional designers.
Abstract: In this research study, an intelligent detection system
to support medical diagnosis and detection of abnormal lesions by
processing endoscopic images is presented. The images used in this
study have been obtained using the M2A Swallowable Imaging
Capsule - a patented, video color-imaging disposable capsule.
Schemes have been developed to extract texture features from the
fuzzy texture spectra in the chromatic and achromatic domains for a
selected region of interest from each color component histogram of
endoscopic images. The implementation of an advanced fuzzy
inference neural network which combines fuzzy systems and
artificial neural networks and the concept of fusion of multiple
classifiers dedicated to specific feature parameters have been also
adopted in this paper. The achieved high detection accuracy of the
proposed system has provided thus an indication that such intelligent
schemes could be used as a supplementary diagnostic tool in
endoscopy.
Abstract: Modeling product configurations needs large amounts of knowledge about technical and marketing restrictions on the product. Previous attempts to automate product configurations concentrate on representations and management of the knowledge for specific domains in fixed and isolated computing environments. Since the knowledge about product configurations is subject to continuous change and hard to express, these attempts often failed to efficiently manage and exchange the knowledge in collaborative product development. In this paper, XML Topic Map (XTM) is introduced to represent and exchange the knowledge about product configurations in collaborative product development. A product configuration model based on XTM along with its merger and inference facilities enables configuration engineers in collaborative product development to manage and exchange their knowledge efficiently. A prototype implementation is also presented to demonstrate the proposed model can be applied to engineering information systems to exchange the product configuration knowledge.
Abstract: Nonlinear system identification is becoming an important tool which can be used to improve control performance. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for controlling a car. The vehicle must follow a predefined path by supervised learning. Backpropagation gradient descent method was performed to train the ANFIS system. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in controlling the non linear system.
Abstract: We investigated statistical performance of Bayesian inference using maximum entropy and MAP estimation for several models which approximated wave-fronts in remote sensing using SAR interferometry. Using Monte Carlo simulation for a set of wave-fronts generated by assumed true prior, we found that the method of maximum entropy realized the optimal performance around the Bayes-optimal conditions by using model of the true prior and the likelihood representing optical measurement due to the interferometer. Also, we found that the MAP estimation regarded as a deterministic limit of maximum entropy almost achieved the same performance as the Bayes-optimal solution for the set of wave-fronts. Then, we clarified that the MAP estimation perfectly carried out phase unwrapping without using prior information, and also that the MAP estimation realized accurate phase unwrapping using conjugate gradient (CG) method, if we assumed the model of the true prior appropriately.
Abstract: In this research, the researchers have managed to
design a model to investigate the current trend of stock price of the
"IRAN KHODRO corporation" at Tehran Stock Exchange by
utilizing an Adaptive Neuro - Fuzzy Inference system. For the Longterm
Period, a Neuro-Fuzzy with two Triangular membership
functions and four independent Variables including trade volume,
Dividend Per Share (DPS), Price to Earning Ratio (P/E), and also
closing Price and Stock Price fluctuation as an dependent variable are
selected as an optimal model. For the short-term Period, a neureo –
fuzzy model with two triangular membership functions for the first
quarter of a year, two trapezoidal membership functions for the
Second quarter of a year, two Gaussian combination membership
functions for the third quarter of a year and two trapezoidal
membership functions for the fourth quarter of a year were selected
as an optimal model for the stock price forecasting. In addition, three
independent variables including trade volume, price to earning ratio,
closing Stock Price and a dependent variable of stock price
fluctuation were selected as an optimal model. The findings of the
research demonstrate that the trend of stock price could be forecasted
with the lower level of error.
Abstract: Recently the usefulness of Concept Abduction, a novel non-monotonic inference service for Description Logics (DLs), has been argued in the context of ontology-based applications such as semantic matchmaking and resource retrieval. Based on tableau calculus, a method has been proposed to realize this reasoning task in ALN, a description logic that supports simple cardinality restrictions as well as other basic constructors. However, in many ontology-based systems, the representation of ontology would require expressive formalisms for capturing domain-specific constraints, this language is not sufficient. In order to increase the applicability of the abductive reasoning method in such contexts, we would like to present in the scope of this paper an extension of the tableaux-based algorithm for dealing with concepts represented inALCQ, the description logic that extends ALN with full concept negation and quantified number restrictions.
Abstract: The Model for Knowledge Base of Computational Objects
(KBCO model) has been successfully applied to represent the
knowledge of human like Plane Geometry, Physical, Calculus. However,
the original model cannot easyly apply in inorganic chemistry
field because of the knowledge specific problems. So, the aim of
this article is to introduce how we extend the Computional Object
(Com-Object) in KBCO model, kinds of fact, problems model, and
inference algorithms to develop a program for solving problems
in inorganic chemistry. Our purpose is to develop the application
that can help students in their study inorganic chemistry at schools.
This application was built successful by using Maple, C# and WPF
technology. It can solve automatically problems and give human
readable solution agree with those writting by students and teachers.
Abstract: Bond Graph as a unified multidisciplinary tool is widely
used not only for dynamic modelling but also for Fault Detection and
Isolation because of its structural and causal proprieties. A binary
Fault Signature Matrix is systematically generated but to make the
final binary decision is not always feasible because of the problems
revealed by such method. The purpose of this paper is introducing a
methodology for the improvement of the classical binary method of
decision-making, so that the unknown and identical failure signatures
can be treated to improve the robustness. This approach consists of
associating the evaluated residuals and the components reliability data
to build a Hybrid Bayesian Network. This network is used in two
distinct inference procedures: one for the continuous part and the
other for the discrete part. The continuous nodes of the network are
the prior probabilities of the components failures, which are used by
the inference procedure on the discrete part to compute the posterior
probabilities of the failures. The developed methodology is applied
to a real steam generator pilot process.
Abstract: The phylogenetic analysis using the most conservative
portions of 18S rRNA gene revealed the phylogenetic relationship
among the two populations where DNA divergence showed that the
nucleotides diversity value were -0.00838 for the Tanjung Dawai,
Kedah and -0.00708 for the Cherating, Pahang populations
respectively. The net nucleotide divergence among populations (Da)
was -0.0073 indicating a low polymorphism among the populations
studied. Total number of mutations in the Tanjung Dawai, Kedah
samples was higher than Cherating, Pahang samples, which are 73 and
59 respectively while shared mutations across the populations were 8,
and reveal the evolutionary in the genome of Malaysian T. gigas. The
tree topology of both populations inferred using Neigbour-joining
method by comparing 1791 bp of partial 18S rRNA sequence revealed
that T. gigas haplotypes were clustered into seven clades, suggesting
that they are genetically diverse among populations which derived
from a common ancestor.
Abstract: This paper analyzes different techniques of the fine grained security of relational databases for the two variables-data accessibility and inference. Data accessibility measures the amount of data available to the users after applying a security technique on a table. Inference is the proportion of information leakage after suppressing a cell containing secret data. A row containing a secret cell which is suppressed can become a security threat if an intruder generates useful information from the related visible information of the same row. This paper measures data accessibility and inference associated with row, cell, and column level security techniques. Cell level security offers greatest data accessibility as it suppresses secret data only. But on the other hand, there is a high probability of inference in cell level security. Row and column level security techniques have least data accessibility and inference. This paper introduces cell plus innocent security technique that utilizes the cell level security method but suppresses some innocent data to dodge an intruder that a suppressed cell may not necessarily contain secret data. Four variations of the technique namely cell plus innocent 1/4, cell plus innocent 2/4, cell plus innocent 3/4, and cell plus innocent 4/4 respectively have been introduced to suppress innocent data equal to 1/4, 2/4, 3/4, and 4/4 percent of the true secret data inside the database. Results show that the new technique offers better control over data accessibility and inference as compared to the state-of-theart security techniques. This paper further discusses the combination of techniques together to be used. The paper shows that cell plus innocent 1/4, 2/4, and 3/4 techniques can be used as a replacement for the cell level security.
Abstract: To develop a process of extracting pixel values over the using of satellite remote sensing image data in Thailand. It is a very important and effective method of forecasting rainfall. This paper presents an approach for forecasting a possible rainfall area based on pixel values from remote sensing satellite images. First, a method uses an automatic extraction process of the pixel value data from the satellite image sequence. Then, a data process is designed to enable the inference of correlations between pixel value and possible rainfall occurrences. The result, when we have a high averaged pixel value of daily water vapor data, we will also have a high amount of daily rainfall. This suggests that the amount of averaged pixel values can be used as an indicator of raining events. There are some positive associations between pixel values of daily water vapor images and the amount of daily rainfall at each rain-gauge station throughout Thailand. The proposed approach was proven to be a helpful manual for rainfall forecasting from meteorologists by which using automated analyzing and interpreting process of meteorological remote sensing data.
Abstract: On the basis of Bayesian inference using the
maximizer of the posterior marginal estimate, we carry out phase
unwrapping using multiple interferograms via generalized mean-field
theory. Numerical calculations for a typical wave-front in remote
sensing using the synthetic aperture radar interferometry, phase
diagram in hyper-parameter space clarifies that the present method
succeeds in phase unwrapping perfectly under the constraint of
surface- consistency condition, if the interferograms are not corrupted
by any noises. Also, we find that prior is useful for extending a phase
in which phase unwrapping under the constraint of the
surface-consistency condition. These results are quantitatively
confirmed by the Monte Carlo simulation.