Abstract: In this paper, the structural genetic algorithm is used to optimize the neural network to control the joint movements of robotic arm. The robotic arm has also been modeled in 3D and simulated in real-time in MATLAB. It is found that Neural Networks provide a simple and effective way to control the robot tasks. Computer simulation examples are given to illustrate the significance of this method. By combining Genetic Algorithm optimization method and Neural Networks for the given robotic arm with 5 D.O.F. the obtained the results shown that the base joint movements overshooting time without controller was about 0.5 seconds, while with Neural Network controller (optimized with Genetic Algorithm) was about 0.2 seconds, and the population size of 150 gave best results.
Abstract: Markov games can be effectively used to design
controllers for nonlinear systems. The paper presents two novel
controller design algorithms by incorporating ideas from gametheory
literature that address safety and consistency issues of the
'learned' control strategy. A more widely used approach for
controller design is the H∞ optimal control, which suffers from high
computational demand and at times, may be infeasible. We generate
an optimal control policy for the agent (controller) via a simple
Linear Program enabling the controller to learn about the unknown
environment. The controller is facing an unknown environment and
in our formulation this environment corresponds to the behavior rules
of the noise modeled as the opponent. Proposed approaches aim to
achieve 'safe-consistent' and 'safe-universally consistent' controller
behavior by hybridizing 'min-max', 'fictitious play' and 'cautious
fictitious play' approaches drawn from game theory. We empirically
evaluate the approaches on a simulated Inverted Pendulum swing-up
task and compare its performance against standard Q learning.
Abstract: A strip domain decomposition parallel algorithm for fast direct Poisson solver is presented on a 3D Cartesian staggered grid. The parallel algorithm follows the principles of sequential algorithm for fast direct Poisson solver. Both Dirichlet and Neumann boundary conditions are addressed. Several test cases are likewise addressed in order to shed light on accuracy and efficiency in the strip domain parallelization algorithm. Actually the current implementation shows a very high efficiency when dealing with a large grid mesh up to 3.6 * 109 under massive parallel approach, which explicitly demonstrates that the proposed algorithm is ready for massive parallel computing.
Abstract: This paper proposes a new procedure for analyzing means-end chain data in marketing research. Most commonly the collected data is summarized in the Hierarchical Value Map (HVM) illustrating the main attribute-consequence-value linkages. This paper argues that traditionally constructed HVM may give an erroneous impression of the results of a means-end study. To justify the arguments, an alternative procedure to (1) determine the dominant attribute-consequence-value linkages and (2) construct HVM in a precise manner is presented. The current approach makes a contribution to means-end analysis, allowing marketers to address a set of marketing problems, such as advertising strategy.
Abstract: Nejad and Mashinchi (2011) proposed a revision for ranking fuzzy numbers based on the areas of the left and the right sides of a fuzzy number. However, this method still has some shortcomings such as lack of discriminative power to rank similar fuzzy numbers and no guarantee the consistency between the ranking of fuzzy numbers and the ranking of their images. To overcome these drawbacks, we propose an epsilon-deviation degree method based on the left area and the right area of a fuzzy number, and the concept of the centroid point. The main advantage of the new approach is the development of an innovative index value which can be used to consistently evaluate and rank fuzzy numbers. Numerical examples are presented to illustrate the efficiency and superiority of the proposed method.
Abstract: Research in distributed artificial intelligence and multiagent systems consider how a set of distributed entities can interact and coordinate their actions in order to solve a given problem. In this paper an overview of this concept and its evolution is presented particularly its application in the design of intelligent tutoring systems. An intelligent tutor based on the concept of agent and centered specifically on the design of a pedagogue agent is illustrated. Our work has two goals: the first one concerns the architecture aspect and the design of a tutor using multiagent approach. The second one deals particularly with the design of a part of a tutor system: the pedagogue agent.
Abstract: Land shortage for burials is one of many issues that emerge out of accelerated urban growth in most developing Asian cities, including Kuala Lumpur. Despite actions taken by the federal government and local authorities in addressing this issue, there is no strategic solution being formulated. Apart from making provisions for land to be developed as new cemeteries, the future plan is merely to allocate reserve land to accommodate the increasing demands of burial grounds around the city. This paper examines problems that arise from the traditional practices of Malay funerary as well as an insight to current urban practices in managing Muslim burial spaces around Kuala Lumpur metropolitan region. This paper will also provide some solutions through design approach that can be applied to counter the existing issues.
Abstract: The genetic algorithm (GA) based solution techniques
are found suitable for optimization because of their ability of
simultaneous multidimensional search. Many GA-variants have been
tried in the past to solve optimal power flow (OPF), one of the
nonlinear problems of electric power system. The issues like
convergence speed and accuracy of the optimal solution obtained
after number of generations using GA techniques and handling
system constraints in OPF are subjects of discussion. The results
obtained for GA-Fuzzy OPF on various power systems have shown
faster convergence and lesser generation costs as compared to other
approaches. This paper presents an enhanced GA-Fuzzy OPF (EGAOPF)
using penalty factors to handle line flow constraints and load
bus voltage limits for both normal network and contingency case
with congestion. In addition to crossover and mutation rate
adaptation scheme that adapts crossover and mutation probabilities
for each generation based on fitness values of previous generations, a
block swap operator is also incorporated in proposed EGA-OPF. The
line flow limits and load bus voltage magnitude limits are handled by
incorporating line overflow and load voltage penalty factors
respectively in each chromosome fitness function. The effects of
different penalty factors settings are also analyzed under contingent
state.
Abstract: In this paper, a nonlinear acoustic echo cancellation
(AEC) system is proposed, whereby 3rd order Volterra filtering is
utilized along with a variable step-size Gauss-Seidel pseudo affine
projection (VSSGS-PAP) algorithm. In particular, the proposed
nonlinear AEC system is developed by considering a double-talk
situation with near-end signal variation. Simulation results
demonstrate that the proposed approach yields better nonlinear AEC
performance than conventional approaches.
Abstract: In the past few years, the use of wireless sensor networks (WSNs) potentially increased in applications such as intrusion detection, forest fire detection, disaster management and battle field. Sensor nodes are generally battery operated low cost devices. The key challenge in the design and operation of WSNs is to prolong the network life time by reducing the energy consumption among sensor nodes. Node clustering is one of the most promising techniques for energy conservation. This paper presents a novel clustering algorithm which maximizes the network lifetime by reducing the number of communication among sensor nodes. This approach also includes new distributed cluster formation technique that enables self-organization of large number of nodes, algorithm for maintaining constant number of clusters by prior selection of cluster head and rotating the role of cluster head to evenly distribute the energy load among all sensor nodes.
Abstract: This paper reviews the greenhouse gas emissions of prefabrication elements for residential development in Hong Kong. Prefabrication becomes a common practice in residential development in Hong Kong and is considered as a green approach. In Hong Kong, prefabrication took place at factories in Pearl River Delta. Although prefabrication reduces construction wastage, it might generate more greenhouse gas emission from transportation and manufacturing processes. This study attempts to measure the “cradle to site" greenhouse gas emission from prefabrication elements for a public housing development in Kai Tak area. The findings could help further reduction of greenhouse gas emissions through process improvement.
Abstract: This study deals with a multi-criteria optimization
problem which has been transformed into a single objective
optimization problem using Response Surface Methodology (RSM),
Artificial Neural Network (ANN) and Grey Relational Analyses
(GRA) approach. Grey-RSM and Grey-ANN are hybrid techniques
which can be used for solving multi-criteria optimization problem.
There have been two main purposes of this research as follows.
1. To determine optimum and robust fiber dyeing process
conditions by using RSM and ANN based on GRA,
2. To obtain the best suitable model by comparing models
developed by different methodologies.
The design variables for fiber dyeing process in textile are
temperature, time, softener, anti-static, material quantity, pH,
retarder, and dispergator. The quality characteristics to be evaluated
are nominal color consistency of fiber, maximum strength of fiber,
minimum color of dyeing solution. GRA-RSM with exact level
value, GRA-RSM with interval level value and GRA-ANN models
were compared based on GRA output value and MSE (Mean Square
Error) performance measurement of outputs with each other. As a
result, GRA-ANN with interval value model seems to be suitable
reducing the variation of dyeing process for GRA output value of the
model.
Abstract: Multirate multimedia delivery applications in multihop Wireless Mesh Network (WMN) are data redundant and delay-sensitive, which brings a lot of challenges for designing efficient transmission systems. In this paper, we propose a new cross layer resource allocation scheme to minimize the receiver side distortion within the delay bound requirements, by exploring application layer Position and Value (P-V) diversity as well as the multihop Effective Capacity (EC). We specifically consider image transmission optimization here. First of all, the maximum supportable source traffic rate is identified by exploring the multihop Effective Capacity (EC) model. Furthermore, the optimal source coding rate is selected according to the P-V diversity of multirate media streaming, which significantly increases the decoded media quality. Simulation results show the proposed approach improved media quality significantly compared with traditional approaches under the same QoS requirements.
Abstract: This paper explores the implementation of adaptive
coding and modulation schemes for Multiple-Input Multiple-Output
Orthogonal Frequency Division Multiplexing (MIMO-OFDM) feedback
systems. Adaptive coding and modulation enables robust and
spectrally-efficient transmission over time-varying channels. The basic
premise is to estimate the channel at the receiver and feed this estimate
back to the transmitter, so that the transmission scheme can be
adapted relative to the channel characteristics. Two types of codebook
based channel feedback techniques are used in this work. The longterm
and short-term CSI at the transmitter is used for efficient channel
utilization. OFDM is a powerful technique employed in communication
systems suffering from frequency selectivity. Combined with
multiple antennas at the transmitter and receiver, OFDM proves to be
robust against delay spread. Moreover, it leads to significant data rates
with improved bit error performance over links having only a single
antenna at both the transmitter and receiver. The coded modulation
increases the effective transmit power relative to uncoded variablerate
variable-power MQAM performance for MIMO-OFDM feedback
system. Hence proposed arrangement becomes an attractive approach
to achieve enhanced spectral efficiency and improved error rate
performance for next generation high speed wireless communication
systems.
Abstract: Science parks are often established to drive regional
economic growth, especially in countries with emerging economies.
However, mixed findings regarding the performances of science park
firms are found in the literature. This study tries to explain these
mixed findings by taking a relational approach and exploring
(un)intended knowledge transfers between new technology-based
firms (NTBFs) in the emerging South African economy. Moreover,
the innovation outcomes of these NTBFs are examined by using a
multi-dimensional construct. Results show that science park location
plays a significant role in explaining innovative sales, but is
insignificant when a different indicator of innovation outcomes is
used. Furthermore, only for innovations that are new to the firms,
both science park location and intended knowledge transfer via
informal business relationships have a positive impact; whereas
social relationships have a negative impact.
Abstract: Mobile Learning (M-Learning) is a new technology
which is to enhance current learning practices and activities for all
people especially students and academic practitioners UTP is
currently, implemented two types of learning styles which are
conventional and electronic learning. In order to improve current
learning approaches, it is necessary for UTP to implement m-learning
in UTP. This paper presents a study on the students- perceptions on
mobile utilization in the learning practices in UTP. Besides, this
paper also presents a survey that was conducted among 82 students
from System Analysis and Design (SAD) course in UTP. The survey
includes basic information of mobile devices that have been used by
the students, opinions on current learning practices and also the
opinions regarding the m-learning implementation in the current
learning practices especially in SAD course. Based on the results of
the survey, majority of the students are using the mobile devices that
can support m-learning environment. Other than that, students also
agreed that current learning practices are ineffective and they believe
that m-learning utilization can improve the effectiveness of current
learning practices.
Abstract: This paper discusses the approach of real-time
controlling of the energy management system using the data
acquisition tool of LabVIEW. The main idea of this inspiration was
to interface the Station (PC) with the system and publish the data on
internet using LabVIEW. In this venture, controlling and switching of
3 phase AC loads are effectively and efficiently done. The phases are
also sensed through devices. In case of any failure the attached
generator starts functioning automatically. The computer sends
command to the system and system respond to the request. The
modern feature is to access and control the system world-wide using
world wide web (internet). This controlling can be done at any time
from anywhere to effectively use the energy especially in developing
countries where energy management is a big problem. In this system
totally integrated devices are used to operate via remote location.
Abstract: Gas flaring is one of the most GHG emitting sources in the oil and gas industries. It is also a major way for wasting such an energy that could be better utilized and even generates revenue. Minimize flaring is an effective approach for reducing GHG emissions and also conserving energy in flaring systems. Integrating waste and flared gases into the fuel gas networks (FGN) of refineries is an efficient tool. A fuel gas network collects fuel gases from various source streams and mixes them in an optimal manner, and supplies them to different fuel sinks such as furnaces, boilers, turbines, etc. In this article we use fuel gas network model proposed by Hasan et al. as a base model and modify some of its features and add constraints on emission pollution by gas flaring to reduce GHG emissions as possible. Results for a refinery case study showed that integration of flare gas stream with waste and natural gas streams to construct an optimal FGN can significantly reduce total annualized cost and flaring emissions.
Abstract: There are several approaches in trying to solve the
Quantitative 1Structure-Activity Relationship (QSAR) problem.
These approaches are based either on statistical methods or on
predictive data mining. Among the statistical methods, one should
consider regression analysis, pattern recognition (such as cluster
analysis, factor analysis and principal components analysis) or partial
least squares. Predictive data mining techniques use either neural
networks, or genetic programming, or neuro-fuzzy knowledge. These
approaches have a low explanatory capability or non at all. This
paper attempts to establish a new approach in solving QSAR
problems using descriptive data mining. This way, the relationship
between the chemical properties and the activity of a substance
would be comprehensibly modeled.
Abstract: Many real-world data sets consist of a very high dimensional feature space. Most clustering techniques use the distance or similarity between objects as a measure to build clusters. But in high dimensional spaces, distances between points become relatively uniform. In such cases, density based approaches may give better results. Subspace Clustering algorithms automatically identify lower dimensional subspaces of the higher dimensional feature space in which clusters exist. In this paper, we propose a new clustering algorithm, ISC – Intelligent Subspace Clustering, which tries to overcome three major limitations of the existing state-of-art techniques. ISC determines the input parameter such as є – distance at various levels of Subspace Clustering which helps in finding meaningful clusters. The uniform parameters approach is not suitable for different kind of databases. ISC implements dynamic and adaptive determination of Meaningful clustering parameters based on hierarchical filtering approach. Third and most important feature of ISC is the ability of incremental learning and dynamic inclusion and exclusions of subspaces which lead to better cluster formation.