Abstract: The use of human hand as a natural interface for humancomputer interaction (HCI) serves as the motivation for research in hand gesture recognition. Vision-based hand gesture recognition involves visual analysis of hand shape, position and/or movement. In this paper, we use the concept of object-based video abstraction for segmenting the frames into video object planes (VOPs), as used in MPEG-4, with each VOP corresponding to one semantically meaningful hand position. Next, the key VOPs are selected on the basis of the amount of change in hand shape – for a given key frame in the sequence the next key frame is the one in which the hand changes its shape significantly. Thus, an entire video clip is transformed into a small number of representative frames that are sufficient to represent a gesture sequence. Subsequently, we model a particular gesture as a sequence of key frames each bearing information about its duration. These constitute a finite state machine. For recognition, the states of the incoming gesture sequence are matched with the states of all different FSMs contained in the database of gesture vocabulary. The core idea of our proposed representation is that redundant frames of the gesture video sequence bear only the temporal information of a gesture and hence discarded for computational efficiency. Experimental results obtained demonstrate the effectiveness of our proposed scheme for key frame extraction, subsequent gesture summarization and finally gesture recognition.
Abstract: The understanding of the system level of biological behavior and phenomenon variously needs some elements such as gene sequence, protein structure, gene functions and metabolic pathways. Challenging problems are representing, learning and reasoning about these biochemical reactions, gene and protein structure, genotype and relation between the phenotype, and expression system on those interactions. The goal of our work is to understand the behaviors of the interactions networks and to model their evolution in time and in space. We propose in this study an ontological meta-model for the knowledge representation of the genetic regulatory networks. Ontology in artificial intelligence means the fundamental categories and relations that provide a framework for knowledge models. Domain ontology's are now commonly used to enable heterogeneous information resources, such as knowledge-based systems, to communicate with each other. The interest of our model is to represent the spatial, temporal and spatio-temporal knowledge. We validated our propositions in the genetic regulatory network of the Aarbidosis thaliana flower
Abstract: This research deals with investigations on the “Active
Generator" under rotor speed variations and output frequency
control. It runs at turbine speed and it is connected to a three phase
electrical power grid which has its own frequency different from
turbine frequency. In this regard the set composed of a four phase
synchronous generator and a natural commutated matrix converter
(NCMC) made with thyristors, is called active generator. It replaces a
classical mechanical gearbox which introduces many drawbacks. The
main idea in this article is the presentation of frequency control at
grid side when turbine runs at variable speed. Frequency control has
been done by linear and step variations of the turbine speed. Relation
between turbine speed (frequency) and main grid zero sequence
voltage frequency is presented.
Abstract: We present new finite element methods for Helmholtz and Maxwell equations on general three-dimensional polyhedral meshes, based on domain decomposition with boundary elements on the surfaces of the polyhedral volume elements. The methods use the lowest-order polynomial spaces and produce sparse, symmetric linear systems despite the use of boundary elements. Moreover, piecewise constant coefficients are admissible. The resulting approximation on the element surfaces can be extended throughout the domain via representation formulas. Numerical experiments confirm that the convergence behavior on tetrahedral meshes is comparable to that of standard finite element methods, and equally good performance is attained on more general meshes.
Abstract: The theory of rough sets is generalized by using a
filter. The filter is induced by binary relations and it is used to
generalize the basic rough set concepts. The knowledge
representations and processing of binary relations in the style of
rough set theory are investigated.
Abstract: Automated discovery of Rule is, due to its applicability, one of the most fundamental and important method in KDD. It has been an active research area in the recent past. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and easy to understand systems is Hierarchical Production rule (HPRs) system. A HPR, a standard production rule augmented with generality and specificity information, is of the following form: Decision If < condition> Generality Specificity . HPRs systems are capable of handling taxonomical structures inherent in the knowledge about the real world. This paper focuses on the issue of mining Quantified rules with crisp hierarchical structure using Genetic Programming (GP) approach to knowledge discovery. The post-processing scheme presented in this work uses Quantified production rules as initial individuals of GP and discovers hierarchical structure. In proposed approach rules are quantified by using Dempster Shafer theory. Suitable genetic operators are proposed for the suggested encoding. Based on the Subsumption Matrix(SM), an appropriate fitness function is suggested. Finally, Quantified Hierarchical Production Rules (HPRs) are generated from the discovered hierarchy, using Dempster Shafer theory. Experimental results are presented to demonstrate the performance of the proposed algorithm.
Abstract: This paper proposes a novel multi-format stream grid
architecture for real-time image monitoring system. The system, based
on a three-tier architecture, includes stream receiving unit, stream
processor unit, and presentation unit. It is a distributed computing and
a loose coupling architecture. The benefit is the amount of required
servers can be adjusted depending on the loading of the image
monitoring system. The stream receive unit supports multi capture
source devices and multi-format stream compress encoder. Stream
processor unit includes three modules; they are stream clipping
module, image processing module and image management module.
Presentation unit can display image data on several different platforms.
We verified the proposed grid architecture with an actual test of image
monitoring. We used a fast image matching method with the
adjustable parameters for different monitoring situations. Background
subtraction method is also implemented in the system. Experimental
results showed that the proposed architecture is robust, adaptive, and
powerful in the image monitoring system.
Abstract: The present work deals with the structural analysis of
turbine blades and modeling of turbine blades. A common failure
mode for turbine machines is high cycle of fatigue of compressor and
turbine blades due to high dynamic stresses caused by blade vibration
and resonance within the operation range of the machinery. In this
work, proper damping system will be analyzed to reduce the
vibrating blade. The main focus of the work is the modeling of under
platform damper to evaluate the dynamic analysis of turbine-blade
vibrations. The system is analyzed using Bond graph technique. Bond
graph is one of the most convenient ways to represent a system from
the physical aspect in foreground. It has advantage of putting together
multi-energy domains of a system in a single representation in a
unified manner. The bond graph model of dry friction damper is
simulated on SYMBOLS-shakti® software. In this work, the blades
are modeled as Timoshenko beam. Blade Vibrations under different
working conditions are being analyzed numerically.
Abstract: With the increasing spread of computers and the internet among culturally, linguistically and geographically diverse communities, issues of internationalization and localization and becoming increasingly important. For some of the issues such as different scales for length and temperature, there is a well-developed measurement theory. For others such as date formats no such theory will be possible. This paper fills a gap by developing a measurement theory for a class of scales previously overlooked, based on discrete and interval-valued scales such as spanner and shoe sizes. The paper gives a theoretical foundation for a class of data representation problems.
Abstract: A square matrix over the complex field with non- negative integral trace is called a quasi-permutation matrix. For a finite group G the minimal degree of a faithful representation of G by quasi-permutation matrices over the rationals and the complex numbers are denoted by q(G) and c(G) respectively. Finally r (G) denotes the minimal degree of a faithful rational valued complex character of C. The purpose of this paper is to calculate q(G), c(G) and r(G) for the group S L(2, q) when extended by a certain group of order two.
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: Gabor-based face representation has achieved enormous success in face recognition. This paper addresses a novel algorithm for face recognition using neural networks trained by Gabor features. The system is commenced on convolving a face image with a series of Gabor filter coefficients at different scales and orientations. Two novel contributions of this paper are: scaling of rms contrast and introduction of fuzzily skewed filter. The neural network employed for face recognition is based on the multilayer perceptron (MLP) architecture with backpropagation algorithm and incorporates the convolution filter response of Gabor jet. The effectiveness of the algorithm has been justified over a face database with images captured at different illumination conditions.
Abstract: A new data fusion method called joint probability density matrix (JPDM) is proposed, which can associate and fuse measurements from spatially distributed heterogeneous sensors to identify the real target in a surveillance region. Using the probabilistic grids representation, we numerically combine the uncertainty regions of all the measurements in a general framework. The NP-hard multisensor data fusion problem has been converted to a peak picking problem in the grids map. Unlike most of the existing data fusion method, the JPDM method dose not need association processing, and will not lead to combinatorial explosion. Its convergence to the CRLB with a diminishing grid size has been proved. Simulation results are presented to illustrate the effectiveness of the proposed technique.
Abstract: Modern culture, based on disinhibition of cultural trends and on heterodirection, is promoting openmindedness attitudes towards ethnic diversity, but on the other hand also new forms of social representations of the foreigner. Social representation is situated between the psychic field and the social one; it is the representation of oneself and of the other one, hanging between social categories and individual inner world. We will produce the results of a research on the representation of the foreigner, built on the type of prejudice prevailing among middle-low or middle-high educational qualification subjects, in which prejudicial attitudes seem to descend from precise mental images of the foreigner.
Abstract: Since the presentation of the backpropagation algorithm, a vast variety of improvements of the technique for training a feed forward neural networks have been proposed. This article focuses on two classes of acceleration techniques, one is known as Local Adaptive Techniques that are based on weightspecific only, such as the temporal behavior of the partial derivative of the current weight. The other, known as Dynamic Adaptation Methods, which dynamically adapts the momentum factors, α, and learning rate, η, with respect to the iteration number or gradient. Some of most popular learning algorithms are described. These techniques have been implemented and tested on several problems and measured in terms of gradient and error function evaluation, and percentage of success. Numerical evidence shows that these techniques improve the convergence of the Backpropagation algorithm.
Abstract: This paper deals with an adaptive multiuser detector for direct sequence code division multiple-access (DS-CDMA) systems. A modified receiver, precombinig LMMSE is considered under time varying channel environment. Detector updating is performed with two criterions, mean square estimation (MSE) and MOE optimization technique. The adaptive implementation issues of these two schemes are quite different. MSE criterion updates the filter weights by minimizing error between data vector and adaptive vector. MOE criterion together with canonical representation of the detector results in a constrained optimization problem. Even though the canonical representation is very complicated under time varying channels, it is analyzed with assumption of average power profile of multipath replicas of user of interest. The performance of both schemes is studied for practical SNR conditions. Results show that for poor SNR, MSE precombining LMMSE is better than the blind precombining LMMSE but for greater SNR, MOE scheme outperforms with better result.
Abstract: Program slicing is the task of finding all statements in a program that directly or indirectly influence the value of a variable occurrence. The set of statements that can affect the value of a variable at some point in a program is called a program slice. In several software engineering applications, such as program debugging and measuring program cohesion and parallelism, several slices are computed at different program points. In this paper, algorithms are introduced to compute all backward and forward static slices of a computer program by traversing the program representation graph once. The program representation graph used in this paper is called Program Dependence Graph (PDG). We have conducted an experimental comparison study using 25 software modules to show the effectiveness of the introduced algorithm for computing all backward static slices over single-point slicing approaches in computing the parallelism and functional cohesion of program modules. The effectiveness of the algorithm is measured in terms of time execution and number of traversed PDG edges. The comparison study results indicate that using the introduced algorithm considerably saves the slicing time and effort required to measure module parallelism and functional cohesion.
Abstract: Software reuse can be considered as the most realistic
and promising way to improve software engineering productivity and
quality. Automated assistance for software reuse involves the
representation, classification, retrieval and adaptation of components.
The representation and retrieval of components are important to
software reuse in Component-Based on Software Development
(CBSD). However, current industrial component models mainly focus
on the implement techniques and ignore the semantic information
about component, so it is difficult to retrieve the components that
satisfy user-s requirements. This paper presents a method of business
component retrieval based on specification matching to solve the
software reuse of enterprise information system. First, a business
component model oriented reuse is proposed. In our model, the
business data type is represented as sign data type based on XML,
which can express the variable business data type that can describe the
variety of business operations. Based on this model, we propose
specification match relationships in two levels: business operation
level and business component level. In business operation level, we
use input business data types, output business data types and the
taxonomy of business operations evaluate the similarity between
business operations. In the business component level, we propose five
specification matches between business components. To retrieval
reusable business components, we propose the measure of similarity
degrees to calculate the similarities between business components.
Finally, a business component retrieval command like SQL is
proposed to help user to retrieve approximate business components
from component repository.
Abstract: Electricity market activities and a growing demand for electricity have led to heavily stressed power systems. This requires operation of the networks closer to their stability limits. Power system operation is affected by stability related problems, leading to unpredictable system behavior. Voltage stability refers to the ability of a power system to sustain appropriate voltage levels through large and small disturbances. Steady-state voltage stability is concerned with limits on the existence of steady-state operating points for the network. FACTS devices can be utilized to increase the transmission capacity, the stability margin and dynamic behavior or serve to ensure improved power quality. Their main capabilities are reactive power compensation, voltage control and power flow control. Among the FACTS controllers, Static Var Compensator (SVC) provides fast acting dynamic reactive compensation for voltage support during contingency events. In this paper, voltage stability assessment with appropriate representations of tap-changer transformers and SVC is investigated. Integrating both of these devices is the main topic of this paper. Effect of the presence of tap-changing transformers on static VAR compensator controller parameters and ratings necessary to stabilize load voltages at certain values are highlighted. The interrelation between transformer off nominal tap ratios and the SVC controller gains and droop slopes and the SVC rating are found. P-V curves are constructed to calculate loadability margins.
Abstract: We depend upon explanation in order to “make sense"
out of our world. And, making sense is all the more important when
dealing with change. But, what happens if our explanations are
wrong? This question is examined with respect to two types of
explanatory model. Models based on labels and categories we shall
refer to as “representations." More complex models involving
stories, multiple algorithms, rules of thumb, questions, ambiguity we
shall refer to as “compressions." Both compressions and
representations are reductions. But representations are far more
reductive than compressions. Representations can be treated as a set
of defined meanings – coherence with regard to a representation is
the degree of fidelity between the item in question and the definition
of the representation, of the label. By contrast, compressions contain
enough degrees of freedom and ambiguity to allow us to make
internal predictions so that we may determine our potential actions in
the possibility space. Compressions are explanatory via mechanism.
Representations are explanatory via category. Managers are often
confusing their evocation of a representation (category inclusion) as
the creation of a context of compression (description of mechanism).
When this type of explanatory error occurs, more errors follow. In
the drive for efficiency such substitutions are all too often proclaimed
– at the manager-s peril..