Abstract: Inverse kinematics analysis plays an important role in developing a robot manipulator. But it is not too easy to derive the inverse kinematic equation of a robot manipulator especially robot manipulator which has numerous degree of freedom. This paper describes an application of Artificial Neural Network for modeling the inverse kinematics equation of a robot manipulator. In this case, the robot has three degree of freedoms and the robot was implemented for drilling a printed circuit board. The artificial neural network architecture used for modeling is a multilayer perceptron networks with steepest descent backpropagation training algorithm. The designed artificial neural network has 2 inputs, 2 outputs and varies in number of hidden layer. Experiments were done in variation of number of hidden layer and learning rate. Experimental results show that the best architecture of artificial neural network used for modeling inverse kinematics of is multilayer perceptron with 1 hidden layer and 38 neurons per hidden layer. This network resulted a RMSE value of 0.01474.
Abstract: In this research paper we have presented control
architecture for robotic arm movement and trajectory planning using
Fuzzy Logic (FL) and Genetic Algorithms (GAs). This architecture is
used to compensate the uncertainties like; movement, friction and
settling time in robotic arm movement. The genetic algorithms and
fuzzy logic is used to meet the objective of optimal control
movement of robotic arm. This proposed technique represents a
general model for redundant structures and may extend to other
structures. Results show optimal angular movement of joints as result
of evolutionary process. This technique has edge over the other
techniques as minimum mathematics complexity used.
Abstract: This paper presents a means for reducing the torque
variation during the revolution of a vertical-axis water turbine
(VAWaterT) by increasing the blade number. For this purpose, twodimensional
CFD analyses have been performed on a straight-bladed
Darrieus-type rotor. After describing the computational model and
the relative validation procedure, a complete campaign of
simulations, based on full RANS unsteady calculations, is proposed
for a three, four and five-bladed rotor architectures, characterized by
a NACA 0025 airfoil. For each proposed rotor configuration, flow
field characteristics are investigated at several values of tip speed
ratio, allowing a quantification of the influence of blade number on
flow geometric features and dynamic quantities, such as rotor torque
and power. Finally, torque and power curves are compared for the
three analyzed architectures, achieving a quantification of the effect
of blade number on overall rotor performance.
Abstract: Business process model describes process flow of a
business and can be seen as the requirement for developing a
software application. This paper discusses a BPM2CD guideline
which complements the Model Driven Architecture concept by
suggesting how to create a platform-independent software model in
the form of a UML class diagram from a business process model. An
important step is the identification of UML classes from the business
process model. A technique for object-oriented analysis called
domain analysis is borrowed and key concepts in the business
process model will be discovered and proposed as candidate classes
for the class diagram. The paper enhances this step by using ontology
search to help identify important classes for the business domain. As
ontology is a source of knowledge for a particular domain which
itself can link to ontologies of related domains, the search can give a
refined set of candidate classes for the resulting class diagram.
Abstract: Evolutionary Algorithms are population-based,
stochastic search techniques, widely used as efficient global
optimizers. However, many real life optimization problems often
require finding optimal solution to complex high dimensional,
multimodal problems involving computationally very expensive
fitness function evaluations. Use of evolutionary algorithms in such
problem domains is thus practically prohibitive. An attractive
alternative is to build meta models or use an approximation of the
actual fitness functions to be evaluated. These meta models are order
of magnitude cheaper to evaluate compared to the actual function
evaluation. Many regression and interpolation tools are available to
build such meta models. This paper briefly discusses the
architectures and use of such meta-modeling tools in an evolutionary
optimization context. We further present two evolutionary algorithm
frameworks which involve use of meta models for fitness function
evaluation. The first framework, namely the Dynamic Approximate
Fitness based Hybrid EA (DAFHEA) model [14] reduces
computation time by controlled use of meta-models (in this case
approximate model generated by Support Vector Machine
regression) to partially replace the actual function evaluation by
approximate function evaluation. However, the underlying
assumption in DAFHEA is that the training samples for the metamodel
are generated from a single uniform model. This does not take
into account uncertain scenarios involving noisy fitness functions.
The second model, DAFHEA-II, an enhanced version of the original
DAFHEA framework, incorporates a multiple-model based learning
approach for the support vector machine approximator to handle
noisy functions [15]. Empirical results obtained by evaluating the
frameworks using several benchmark functions demonstrate their
efficiency
Abstract: Today we tend to go back to the past to our root
relation to nature. Therefore in search of friendly spaces there are
elements of natural environment introduced as elements of spatial
composition. Though reinvented through the use of the new
substance such as greenery, water etc. made possible by state of the
art technologies, still, in principal, they remain the same. As a result,
sustainable design, based upon the recognized means of composition
in addition to the relation of architecture and urbanism vs. nature
introduces a new aesthetical values into architectural and urban
space.
Abstract: Business transformation initiatives are required by
any organization to jump from its normal mode of operation to the
one that is suitable for the change in the environment such as
competitive pressures, regulatory requirements, changes in labor
market, etc., or internal such as changes in strategy/vision, changes in
the capability, change in the management, etc. Recent advances in
information technology in automating the business processes have
the potential to transform an organization to provide it with a
sustained competitive advantage. Process constitutes the skeleton of
a business. Thus, for a business to exist and compete well, it is
essential for the skeleton to be robust and agile. This paper details
“transformation" from a business perspective, methodologies to bring
about an effective transformation, process-based transformation, and
the role of services computing in this. Further, it details the benefits
that could be achieved through services computing.
Abstract: One of the most important aspects expected from ERP systems is to integrate various operations existing in administrative, financial, commercial, human resources, and production departments of the consumer organization. Also, it is often needed to integrate the new ERP system with the organization legacy systems when implementing the ERP package in the organization. Without relying on an appropriate software architecture to realize the required integration, ERP implementation processes become error prone and time consuming; in some cases, the ERP implementation may even encounters serious risks. In this paper, we propose a new architecture that is based on the agent oriented vision and supplies the integration expected from ERP systems using several independent but cooperator agents. Besides integration which is the main issue of this paper, the presented architecture will address some aspects of intelligence and learning capabilities existing in ERP systems
Abstract: Speech corpus is one of the major components in a
Speech Processing System where one of the primary requirements
is to recognize an input sample. The quality and details captured
in speech corpus directly affects the precision of recognition. The
current work proposes a platform for speech corpus generation using
an adaptive LMS filter and LPC cepstrum, as a part of an ANN
based Speech Recognition System which is exclusively designed to
recognize isolated numerals of Assamese language- a major language
in the North Eastern part of India. The work focuses on designing an
optimal feature extraction block and a few ANN based cooperative
architectures so that the performance of the Speech Recognition
System can be improved.
Abstract: Chakri Maha Prasart Throne Hall is the important
Audience hall in Grand Palace, Bangkok, Thailand which was
established in the early reign of King Chulalongkorn (King Rama V)
in 1882. The Throne was designed with the distinguished architecture
by significant blending of Western and Thai Traditional styles under
the Thai Social changing in Colony Era and Thai traditional culture.
The western style was represented of modernization and civilization as
the other European countries. In the other hand, Thai traditional
architecture style with national emblem or Royal emblem was shown
the status and power of Thai King as the Thai believes and culture.
Abstract: The use of artificial neural network (ANN) modeling
for prediction and forecasting variables in water resources
engineering are being increasing rapidly. Infrastructural applications
of ANN in terms of selection of inputs, architecture of networks,
training algorithms, and selection of training parameters in different
types of neural networks used in water resources engineering have
been reported. ANN modeling conducted for water resources
engineering variables (river sediment and discharge) published in
high impact journals since 2002 to 2011 have been examined and
presented in this review. ANN is a vigorous technique to develop
immense relationship between the input and output variables, and
able to extract complex behavior between the water resources
variables such as river sediment and discharge. It can produce robust
prediction results for many of the water resources engineering
problems by appropriate learning from a set of examples. It is
important to have a good understanding of the input and output
variables from a statistical analysis of the data before network
modeling, which can facilitate to design an efficient network. An
appropriate training based ANN model is able to adopt the physical
understanding between the variables and may generate more effective
results than conventional prediction techniques.
Abstract: This paper proposes a low-cost reconfigurable
architecture for AES algorithm. The proposed architecture separates
SubBytes and MixColumns into two parallel data path, and supports
different bit-width operation for this two data path. As a result, different number of S-box can be supported in this architecture. The
throughput and power consumption can be adjusted by changing the
number of S-box running in this design. Using the TSMC 0.18μm CMOS standard cell library, a very low-cost implementation of 7K
Gates is obtained under 182MHz frequency. The maximum throughput is 360Mbps while using 4 S-Box simultaneously, and the
minimum throughput is 114Mbps while only using 1 S-Box
Abstract: According as the Architecture, Engineering and Construction (AEC) Industry projects have grown more complex and larger, the number of utilization of BIM for 3D design and simulation is increasing significantly. Therefore, typical applications of BIM such as clash detection and alternative measures based on 3-dimenstional planning are expanded to process management, cost and quantity management, structural analysis, check for regulation, and various domains for virtual design and construction. Presently, commercial BIM software is operated on single-user environment, so initial cost is so high and the investment may be wasted frequently. Cloud computing that is a next-generation internet technology enables simple internet devices (such as PC, Tablet, Smart phone etc) to use services and resources of BIM software. In this paper, we suggested developing method of the BIM software based on cloud computing environment in order to expand utilization of BIM and reduce cost of BIM software. First, for the benchmarking, we surveyed successful case of BIM and cloud computing. And we analyzed needs and opportunities of BIM and cloud computing in AEC Industry. Finally, we suggested main functions of BIM software based on cloud computing environment and developed a simple prototype of cloud computing BIM software for basic BIM model viewing.
Abstract: Computers are being integrated in the various aspects
of human every day life in different shapes and abilities. This fact
has intensified a requirement for the software development
technologies which is ability to be: 1) portable, 2) adaptable, and 3)
simple to develop. This problem is also known as the Pervasive
Computing Problem (PCP) which can be implemented in different
ways, each has its own pros and cons and Context Oriented
Programming (COP) is one of the methods to address the PCP.
In this paper a design for a COP framework, a context aware
framework, is presented which has eliminated weak points of a
previous design based on interpreter languages, while introducing the
compiler languages power in implementing these frameworks.
The key point of this improvement is combining COP and
Dependency Injection (DI) techniques. Both old and new frameworks
are analyzed to show advantages and disadvantages. Finally a
simulation of both designs is proposed to indicating that the practical
results agree with the theoretical analysis while the new design runs
almost 8 times faster.
Abstract: In this paper we present an efficient system for
independent speaker speech recognition based on neural network
approach. The proposed architecture comprises two phases: a
preprocessing phase which consists in segmental normalization and
features extraction and a classification phase which uses neural
networks based on nonparametric density estimation namely the
general regression neural network (GRNN). The relative
performances of the proposed model are compared to the similar
recognition systems based on the Multilayer Perceptron (MLP), the
Recurrent Neural Network (RNN) and the well known Discrete
Hidden Markov Model (HMM-VQ) that we have achieved also.
Experimental results obtained with Arabic digits have shown that the
use of nonparametric density estimation with an appropriate
smoothing factor (spread) improves the generalization power of the
neural network. The word error rate (WER) is reduced significantly
over the baseline HMM method. GRNN computation is a successful
alternative to the other neural network and DHMM.
Abstract: The NGN (Next Generation Network), which can
provide advanced multimedia services over an all-IP based network, has been the subject of much attention for years. While there have
been tremendous efforts to develop its architecture and protocols, especially for IMS, which is a key technology of the NGN, it is far
from being widely deployed. However, efforts to create an advanced
signaling infrastructure realizing many requirements have resulted in a
large number of functional components and interactions between those
components. Thus, the carriers are trying to explore effective ways to
deploy IMS while offering value-added services. As one such
approach, we have proposed a self-organizing IMS. A self-organizing
IMS enables IMS functional components and corresponding physical
nodes to adapt dynamically and automatically based on situation such
as network load and available system resources while continuing IMS
operation. To realize this, service continuity for users is an important
requirement when a reconfiguration occurs during operation. In this
paper, we propose a mechanism that will provide service continuity to
users and focus on the implementation and describe performance
evaluation in terms of number of control signaling and processing time
during reconfiguration