Abstract: Resource discovery is one of the chief services of a grid. A new approach to discover the provenances in grid through learning automata has been propounded in this article. The objective of the aforementioned resource-discovery service is to select the resource based upon the user-s applications and the mercantile yardsticks that is to say opting for an originator which can accomplish the user-s tasks in the most economic manner. This novel service is submitted in two phases. We proffered an applicationbased categorization by means of an intelligent nerve-prone plexus. The user in question sets his or her application as the input vector of the nerve-prone nexus. The output vector of the aforesaid network limns the appropriateness of any one of the resource for the presented executive procedure. The most scrimping option out of those put forward in the previous stage which can be coped with to fulfill the task in question is picked out. Te resource choice is carried out by means of the presented algorithm based upon the learning automata.
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: Flexible manufacturing system is a system that is able to respond to changed conditions. In general, this flexibility is divided into two key categories and several subcategories. The first category is the so called machine flexibility which enables to make various products by the given machinery. The second category is routing flexibility enabling to execute the same operation by various machines. Flexible manufacturing systems usually consist of three main parts: CNC machine tools, transport system and control system. A higher level of flexible manufacturing systems is represented by the so called intelligent manufacturing systems.
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: A low cost Short Message System (SMS) based Home security system equipped with motion, smoke, temperature, humidity and light sensors has been studied and tested. The sensors are controlled by a microprocessor PIC 18F4520 through the SMS having password protection code for the secure operation. The user is able to switch light and the appliances and get instant feedback. Also in cases of emergencies such as fire or robbery the system will send alert message to occupant and relevant civil authorities. The operation of the home security has been tested on Vodafone- Fiji network and Digicel Fiji Network for emergency and feedback responses for 25 samples. The experiment showed that it takes about 8-10s for the security system to respond in case of emergency. It takes about 18-22s for the occupant to switch and monitor lights and appliances and then get feedback depending upon the network traffic.
Abstract: This paper presents an intelligent speed control
system based on fuzzy logic for a voltage source PWM inverter-fed
indirect vector controlled induction motor drive. Traditional indirect
vector control system of induction motor introduces conventional PI
regulator in outer speed loop; it is proved that the low precision of the
speed regulator debases the performance of the whole system. To
overcome this problem, replacement of PI controller by an intelligent
controller based on fuzzy set theory is proposed. The performance of
the intelligent controller has been investigated through digital
simulation using MATLAB-SIMULINK package for different
operating conditions such as sudden change in reference speed and
load torque. The simulation results demonstrate that the performance
of the proposed controller is better than that of the conventional PI
controller.
Abstract: This paper deals with dynamic load balancing using PVM. In distributed environment Load Balancing and Heterogeneity are very critical issues and needed to drill down in order to achieve the optimal results and efficiency. Various techniques are being used in order to distribute the load dynamically among different nodes and to deal with heterogeneity. These techniques are using different approaches where Process Migration is basic concept with different optimal flavors. But Process Migration is not an easy job, it impose lot of burden and processing effort in order to track each process in nodes. We will propose a dynamic load balancing technique in which application will intelligently balance the load among different nodes, resulting in efficient use of system and have no overheads of process migration. It would also provide a simple solution to problem of load balancing in heterogeneous environment.
Abstract: Typical Intelligent Decision Support System is 4-based, its design composes of Data Warehouse, Online Analytical Processing, Data Mining and Decision Supporting based on models, which is called Decision Support System Based on Data Warehouse (DSSBDW). This way takes ETL,OLAP and DM as its implementing means, and integrates traditional model-driving DSS and data-driving DSS into a whole. For this kind of problem, this paper analyzes the DSSBDW architecture and DW model, and discusses the following key issues: ETL designing and Realization; metadata managing technology using XML; SQL implementing, optimizing performance, data mapping in OLAP; lastly, it illustrates the designing principle and method of DW in DSSBDW.
Abstract: Various intelligences and inspirations have been
adopted into the iterative searching process called as meta-heuristics.
They intelligently perform the exploration and exploitation in the
solution domain space aiming to efficiently seek near optimal
solutions. In this work, the bee algorithm, inspired by the natural
foraging behaviour of honey bees, was adapted to find the near
optimal solutions of the transportation management system, dynamic
multi-zone dispatching. This problem prepares for an uncertainty and
changing customers- demand. In striving to remain competitive,
transportation system should therefore be flexible in order to cope
with the changes of customers- demand in terms of in-bound and outbound
goods and technological innovations. To remain higher service
level but lower cost management via the minimal imbalance scenario,
the rearrangement penalty of the area, in each zone, including time
periods are also included. However, the performance of the algorithm
depends on the appropriate parameters- setting and need to be
determined and analysed before its implementation. BEE parameters
are determined through the linear constrained response surface
optimisation or LCRSOM and weighted centroid modified simplex
methods or WCMSM. Experimental results were analysed in terms
of best solutions found so far, mean and standard deviation on the
imbalance values including the convergence of the solutions
obtained. It was found that the results obtained from the LCRSOM
were better than those using the WCMSM. However, the average
execution time of experimental run using the LCRSOM was longer
than those using the WCMSM. Finally a recommendation of proper
level settings of BEE parameters for some selected problem sizes is
given as a guideline for future applications.
Abstract: In this paper application of artificial intelligence for
baby and children caring is studied. Then a new idea for injury
prevention and safety announcement is presented by using digital
image processing. The paper presents the structure of the proposed
system. The system determines the possibility of the dangers for
children and babies in yards, gardens and swimming pools or etc. In
the presented idea, multi camera System is used and receiver videos
are processed to find the hazardous areas then the entrance of
children and babies in the determined hazardous areas are analyzed.
In this condition the system does the programmed action capture,
produce alarm or tone or send message.
Abstract: In order to give high expertise the computer aided
design of mechanical systems involves specific activities focused on
processing two type of information: knowledge and data. Expert rule
based knowledge is generally processing qualitative information and
involves searching for proper solutions and their combination into
synthetic variant. Data processing is based on computational models
and it is supposed to be inter-related with reasoning in the knowledge
processing. In this paper an Intelligent Integrated System is proposed,
for the objective of choosing the adequate material. The software is
developed in Prolog – Flex software and takes into account various
constraints that appear in the accurate operation of gears.
Abstract: More and more natural disasters are happening every
year: floods, earthquakes, volcanic eruptions, etc. In order to reduce
the risk of possible damages, governments all around the world are
investing into development of Early Warning Systems (EWS) for
environmental applications. The most important task of the EWS is
identification of the onset of critical situations affecting environment
and population, early enough to inform the authorities and general
public. This paper describes an approach for monitoring of flood
protections systems based on machine learning methods. An
Artificial Intelligence (AI) component has been developed for
detection of abnormal dike behaviour. The AI module has been
integrated into an EWS platform of the UrbanFlood project (EU
Seventh Framework Programme) and validated on real-time
measurements from the sensors installed in a dike.
Abstract: In this contribution an innovative platform is being
presented that integrates intelligent agents and evolutionary
computation techniques in legacy e-learning environments. It
introduces the design and development of a scalable and
interoperable integration platform supporting:
I) various assessment agents for e-learning environments,
II) a specific resource retrieval agent for the provision of
additional information from Internet sources matching the
needs and profile of the specific user and
III) a genetic algorithm designed to extract efficient information
(classifying rules) based on the students- answering input
data.
The agents are implemented in order to provide intelligent
assessment services based on computational intelligence techniques
such as Bayesian Networks and Genetic Algorithms.
The proposed Genetic Algorithm (GA) is used in order to extract
efficient information (classifying rules) based on the students-
answering input data. The idea of using a GA in order to fulfil this
difficult task came from the fact that GAs have been widely used in
applications including classification of unknown data.
The utilization of new and emerging technologies like web
services allows integrating the provided services to any web based
legacy e-learning environment.
Abstract: Knowledge bases are basic components of expert
systems or intelligent computational programs. Knowledge bases
provide knowledge, events that serve deduction activity,
computation and control. Therefore, researching and developing of
models for knowledge representation play an important role in
computer science, especially in Artificial Intelligence Science and
intelligent educational software. In this paper, the extensive
deduction computational model is proposed to design knowledge
bases whose attributes are able to be real values or functional values.
The system can also solve problems based on knowledge bases.
Moreover, the models and algorithms are applied to produce the
educational software for solving alternating current problems or
solving set of equations automatically.
Abstract: Case based reasoning (CBR) methodology presents a foundation for a new technology of building intelligent computeraided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. This paper discusses the CBR methodology, the research issues and technical aspects of implementing intelligent medical diagnoses systems. Successful applications in cancer and heart diseases developed by Medical Informatics Research Group at Ain Shams University are also discussed.
Abstract: In the artificial intelligence field, knowledge
representation and reasoning are important areas for intelligent
systems, especially knowledge base systems and expert systems.
Knowledge representation Methods has an important role in
designing the systems. There have been many models for knowledge
such as semantic networks, conceptual graphs, and neural networks.
These models are useful tools to design intelligent systems. However,
they are not suitable to represent knowledge in the domains of reality
applications. In this paper, new models for knowledge representation
called computational networks will be presented. They have been
used in designing some knowledge base systems in education for
solving problems such as the system that supports studying
knowledge and solving analytic geometry problems, the program for
studying and solving problems in Plane Geometry, the program for
solving problems about alternating current in physics.
Abstract: In this paper we propose an intelligent agent approach
to control the electric power grid at a smaller granularity in order to
give it self-healing capabilities. We develop a method using the
influence model to transform transmission substations into
information processing, analyzing and decision making (intelligent
behavior) units. We also develop a wireless communication method
to deliver real-time uncorrupted information to an intelligent
controller in a power system environment. A combined networking
and information theoretic approach is adopted in meeting both the
delay and error probability requirements. We use a mobile agent
approach in optimizing the achievable information rate vector and in
the distribution of rates to users (sensors). We developed the concept
and the quantitative tools require in the creation of cooperating semiautonomous
subsystems which puts the electric grid on the path
towards intelligent and self-healing system.
Abstract: Traffic management in an urban area is highly facilitated by the knowledge of the traffic conditions in every street or highway involved in the vehicular mobility system. Aim of the paper is to propose a neuro-fuzzy approach able to compute the main parameters of a traffic system, i.e., car density, velocity and flow, by using the images collected by the web-cams located at the crossroads of the traffic network. The performances of this approach encourage its application when the traffic system is far from the saturation. A fuzzy model is also outlined to evaluate when it is suitable to use more accurate, even if more time consuming, algorithms for measuring traffic conditions near to saturation.
Abstract: Built environments have a large impact on environmental sustainability and if it is not considered properly can negatively affect our planet. The application of transformable intelligent building systems that automatically respond to environmental conditions is one of the best ways that can intelligently assist us to create sustainable environment. The significance of this issue is evident as energy crisis and environmental changes has made the sustainability the main concerns in many societies. The aim of this research is to review and evaluate the importance and influence of transformable intelligent structure on the creation of sustainable architecture. Intelligent systems in current buildings provide convenience through automatically responding to changes in environmental conditions, reducing energy dissipation and increase of the lifecycle of buildings. This paper by analyzing significant intelligent building systems will evaluate the potentials of transformable intelligent systems in the creation of sustainable architecture and environment.
Abstract: There are two common types of operational research techniques, optimisation and metaheuristic methods. The latter may be defined as a sequential process that intelligently performs the exploration and exploitation adopted by natural intelligence and strong inspiration to form several iterative searches. An aim is to effectively determine near optimal solutions in a solution space. In this work, a type of metaheuristics called Ant Colonies Optimisation, ACO, inspired by a foraging behaviour of ants was adapted to find optimal solutions of eight non-linear continuous mathematical models. Under a consideration of a solution space in a specified region on each model, sub-solutions may contain global or multiple local optimum. Moreover, the algorithm has several common parameters; number of ants, moves, and iterations, which act as the algorithm-s driver. A series of computational experiments for initialising parameters were conducted through methods of Rigid Simplex, RS, and Modified Simplex, MSM. Experimental results were analysed in terms of the best so far solutions, mean and standard deviation. Finally, they stated a recommendation of proper level settings of ACO parameters for all eight functions. These parameter settings can be applied as a guideline for future uses of ACO. This is to promote an ease of use of ACO in real industrial processes. It was found that the results obtained from MSM were pretty similar to those gained from RS. However, if these results with noise standard deviations of 1 and 3 are compared, MSM will reach optimal solutions more efficiently than RS, in terms of speed of convergence.