Abstract: We introduce an extended resource leveling model that abstracts real life projects that consider specific work ranges for each resource. Contrary to traditional resource leveling problems this model considers scarce resources and multiple objectives: the minimization of the project makespan and the leveling of each resource usage over time. We formulate this model as a multiobjective optimization problem and we propose a multiobjective genetic algorithm-based solver to optimize it. This solver consists in a two-stage process: a main stage where we obtain non-dominated solutions for all the objectives, and a postprocessing stage where we seek to specifically improve the resource leveling of these solutions. We propose an intelligent encoding for the solver that allows including domain specific knowledge in the solving mechanism. The chosen encoding proves to be effective to solve leveling problems with scarce resources and multiple objectives. The outcome of the proposed solvers represent optimized trade-offs (alternatives) that can be later evaluated by a decision maker, this multi-solution approach represents an advantage over the traditional single solution approach. We compare the proposed solver with state-of-art resource leveling methods and we report competitive and performing results.
Abstract: This paper presents a distributed intrusion
detection system IDS, based on the concept of specialized
distributed agents community representing agents with the
same purpose for detecting distributed attacks. The semantic of
intrusion events occurring in a predetermined network has been
defined. The correlation rules referring the process which our
proposed IDS combines the captured events that is distributed
both spatially and temporally. And then the proposed IDS tries
to extract significant and broad patterns for set of well-known
attacks. The primary goal of our work is to provide intrusion
detection and real-time prevention capability against insider
attacks in distributed and fully automated environments.
Abstract: Using electrical machine in conventional vehicles, also called hybrid vehicles, has become a promising control scheme that enables some manners for fuel economy and driver assist for better stability. In this paper, vehicle stability control, fuel economy and Driving/Regeneration braking for a 4WD hybrid vehicle is investigated by using an electrical machine on each non-driven wheels. In front wheels driven vehicles, fuel economy and regenerative braking can be obtained by summing torques applied on rear wheels. On the other hand, unequal torques applied to rear wheels provides enhanced safety and path correction in steering. In this paper, a model with fourteen degrees of freedom is considered for vehicle body, tires and, suspension systems. Thereafter, powertrain subsystems are modeled. Considering an electrical machine on each rear wheel, a fuzzy controller is designed for each driving, braking, and stability conditions. Another fuzzy controller recognizes the vehicle requirements between the driving/regeneration and stability modes. Intelligent vehicle control to multi objective operation and forward simulation are the paper advantages. For reaching to these aims, power management control and yaw moment control will be done by three fuzzy controllers. Also, the above mentioned goals are weighted by another fuzzy sub-controller base on vehicle dynamic. Finally, Simulations performed in MATLAB/SIMULINK environment show that the proposed structure can enhance the vehicle performance in different modes effectively.
Abstract: This paper summaries basic principles and concepts of
intelligent controls, implemented in humanoid robotics as well as
recent algorithms being devised for advanced control of humanoid
robots. Secondly, this paper presents a new approach neuro-fuzzy
system. We have included some simulating results from our
computational intelligence technique that will be applied to our
humanoid robot. Subsequently, we determine a relationship between
joint trajectories and located forces on robot-s foot through a
proposed neuro-fuzzy technique.
Abstract: Recently, a vehicular ad-hoc networks(VANETs) for
Intelligent Transport System(ITS) have become able safety and convenience services surpassing the simple services such as
an electronic toll collection system. To provide the proper services,
VANET needs infrastructure over the country infrastructure. Thus, we have to spend a huge sum of
human resources. In this reason, several studies have been made on the
usage of cellular networks instead of new protocols
this study is to assess a performance evaluation of the
cellular network for VANET. In this paper, the result of a
for the suitability of cellular networks for VANET
experiment, The LTE(Long Term Evolution) of cellular networks found to be most suitable among the others cellular networks
Abstract: Circular knitting machine makes the fabric with more than two knitting tools. Variation of yarn tension between different knitting tools causes different loop length of stitches duration knitting process. In this research, a new intelligent method is applied to control loop length of stitches in various tools based on ideal shape of stitches and real angle of stitches direction while different loop length of stitches causes stitches deformation and deviation those of angle. To measure deviation of stitch direction against variation of tensions, image processing technique was applied to pictures of different fabrics with constant front light. After that, the rate of deformation is translated to needed compensation of loop length cam degree to cure stitches deformation. A fuzzy control algorithm was applied to loop length modification in knitting tools. The presented method was experienced for different knitted fabrics of various structures and yarns. The results show that presented method is useable for control of loop length variation between different knitting tools based on stitch deformation for various knitted fabrics with different fabric structures, densities and yarn types.
Abstract: The internet is constantly expanding. Identifying web
links of interest from web browsers requires users to visit each of the
links listed, individually until a satisfactory link is found, therefore
those users need to evaluate a considerable amount of links before
finding their link of interest; this can be tedious and even
unproductive. By incorporating web assistance, web users could be
benefited from reduced time searching on relevant websites. In this
paper, a rough set approach is presented, which facilitates
classification of unlimited available e-vocabulary, to assist web users
in reducing search times looking for relevant web sites. This
approach includes two methods for identifying relevance data on web
links based on the priority and percentage of relevance. As a result of
these methods, a list of web sites is generated in priority sequence
with an emphasis of the search criteria.
Abstract: Currently, most of distance learning courses can only
deliver standard material to students. Students receive course content
passively which leads to the neglect of the goal of education – “to suit
the teaching to the ability of students". Providing appropriate course
content according to students- ability is the main goal of this paper.
Except offering a series of conventional learning services, abundant
information available, and instant message delivery, a complete online
learning environment should be able to distinguish between students-
ability and provide learning courses that best suit their ability.
However, if a distance learning site contains well-designed course
content and design but fails to provide adaptive courses, students will
gradually loss their interests and confidence in learning and result in
ineffective learning or discontinued learning. In this paper, an
intelligent tutoring system is proposed and it consists of several
modules working cooperatively in order to build an adaptive learning
environment for distance education. The operation of the system is
based on the result of Self-Organizing Map (SOM) to divide students
into different groups according to their learning ability and learning
interests and then provide them with suitable course content.
Accordingly, the problem of information overload and internet traffic
problem can be solved because the amount of traffic accessing the
same content is reduced.
Abstract: Motion detection is a basic operation in the selection of significant segments of the video signals. For an effective Human Computer Intelligent Interaction, the computer needs to recognize the motion and track the moving object. Here an efficient neural network system is proposed for motion detection from the static background. This method mainly consists of four parts like Frame Separation, Rough Motion Detection, Network Formation and Training, Object Tracking. This paper can be used to verify real time detections in such a way that it can be used in defense applications, bio-medical applications and robotics. This can also be used for obtaining detection information related to the size, location and direction of motion of moving objects for assessment purposes. The time taken for video tracking by this Neural Network is only few seconds.
Abstract: In this paper, an intelligent algorithm for optimal
document archiving is presented. It is kown that electronic archives
are very important for information system management. Minimizing
the size of the stored data in electronic archive is a main issue to
reduce the physical storage area. Here, the effect of different types of
Arabic fonts on electronic archives size is discussed. Simulation
results show that PDF is the best file format for storage of the Arabic
documents in electronic archive. Furthermore, fast information
detection in a given PDF file is introduced. Such approach uses fast
neural networks (FNNs) implemented in the frequency domain. The
operation of these networks relies on performing cross correlation in
the frequency domain rather than spatial one. It is proved
mathematically and practically that the number of computation steps
required for the presented FNNs is less than that needed by
conventional neural networks (CNNs). Simulation results using
MATLAB confirm the theoretical computations.
Abstract: In this paper, a worm-like micro robot designed for inpipe
application with intelligent active force control (AFC) capability
is modelled and simulated. The motion of the micro robot is based on
an impact drive mechanism (IDM) that is actuated using piezoelectric
device. The trajectory tracking performance of the modelled micro
robot is initially experimented via a conventional proportionalintegral-
derivative (PID) controller in which the dynamic response of
the robot system subjected to different input excitations is
investigated. Subsequently, a robust intelligent method known as
active force control with fuzzy logic (AFCFL) is later incorporated
into the PID scheme to enhance the system performance by
compensating the unwanted disturbances due to the interaction of the
robot with its environment. Results show that the proposed AFCFL
scheme is far superior than the PID control counterpart in terms of
the system-s tracking capability in the wake of the disturbances.
Abstract: Artificial Immune System (AIS) is relatively naive paradigm for intelligent computations. The inspiration for AIS is derived from natural Immune System (IS). Classically it is believed that IS strives to discriminate between self and non-self. Most of the existing AIS research is based on this approach. Danger Theory (DT) argues this approach and proposes that IS fights against danger producing elements and tolerates others. We, the computational researchers, are not concerned with the arguments among immunologists but try to extract from it novel abstractions for intelligent computation. This paper aims to follow DT inspiration for intelligent data processing. The approach may introduce new avenue in intelligent processing. The data used is system calls data that is potentially significant in intrusion detection applications.
Abstract: The paper attempts to contribute to the largely
neglected social and anthropological discussion of technology development on the one hand, and to redirecting the emphasis in
anthropology from primitive and exotic societies to problems of high
relevance in contemporary era and how technology is used in
everyday life. It draws upon multidimensional models of intelligence
and ideal type formation. It is argued that the predominance of
computational and cognitive cosmovisions have led to technology alienation. Injection of communicative competence in artificially
intelligent systems and identity technologies in the coming
information society are analyzed
Abstract: The gas safety management system using an
intelligent gas meter we proposed is to monitor flow and
pressure of gas, earthquake, temperature, smoke and leak of
methane. Then our system takes safety measures to protect a
serious risk by the result of an event, to communicate with a
wall-pad including a gateway by zigbee network in buildings
and to report the event to user by the safety management
program in a server. Also, the inner cutoff valve of an
intelligent gas meter is operated if any event occurred or
abnormal at each sensor.
Abstract: It is by reason of the unified measure of varieties of resources and the unified processing of the disposal of varieties of resources, that these closely related three of new basic models called the resources assembled node and the disposition integrated node as well as the intelligent organizing node are put forth in this paper; the three closely related quantities of integrative analytical mechanics including the disposal intensity and disposal- weighted intensity as well as the charge of resource charge are set; and then the resources assembled space and the disposition integrated space as well as the intelligent organizing space are put forth. The system of fundamental equations and model of complete factor synergetics is preliminarily approached for the general situation in this paper, to form the analytical base of complete factor synergetics. By the essential variables constituting this system of equations we should set twenty variables respectively with relation to the essential dynamical effect, external synergetic action and internal synergetic action of the system.
Abstract: The conventional GA combined with a local search
algorithm, such as the 2-OPT, forms a hybrid genetic algorithm(HGA)
for the traveling salesman problem (TSP). However, the geometric
properties which are problem specific knowledge can be used to
improve the search process of the HGA. Some tour segments (edges)
of TSPs are fine while some maybe too long to appear in a short tour.
This knowledge could constrain GAs to work out with fine tour
segments without considering long tour segments as often.
Consequently, a new algorithm is proposed, called intelligent-OPT
hybrid genetic algorithm (IOHGA), to improve the GA and the 2-OPT
algorithm in order to reduce the search time for the optimal solution.
Based on the geometric properties, all the tour segments are assigned
2-level priorities to distinguish between good and bad genes. A
simulation study was conducted to evaluate the performance of the
IOHGA. The experimental results indicate that in general the IOHGA
could obtain near-optimal solutions with less time and better accuracy
than the hybrid genetic algorithm with simulated annealing algorithm
(HGA(SA)).
Abstract: Active vibration control is an important problem in
structures. The objective of active vibration control is to reduce the vibrations of a system by automatic modification of the system-s
structural response. In this paper, the modeling and design of a fast
output sampling feedback controller for a smart flexible beam system embedded with shear sensors and actuators for SISO system using
Timoshenko beam theory is proposed. FEM theory, Timoshenko beam theory and the state space techniques are used to model the
aluminum cantilever beam. For the SISO case, the beam is divided into 5 finite elements and the control actuator is placed at finite
element position 1, whereas the sensor is varied from position 2 to 5, i.e., from the nearby fixed end to the free end. Controllers are
designed using FOS method and the performance of the designed FOS controller is evaluated for vibration control for 4 SISO models
of the same plant. The effect of placing the sensor at different locations on the beam is observed and the performance of the
controller is evaluated for vibration control. Some of the limitations of the Euler-Bernoulli theory such as the neglection of shear and
axial displacement are being considered here, thus giving rise to an accurate beam model. Embedded shear sensors and actuators have
been considered in this paper instead of the surface mounted sensors
and actuators for vibration suppression because of lot of advantages. In controlling the vibration modes, the first three dominant modes of
vibration of the system are considered.
Abstract: Heart disease (HD) is a major cause of morbidity and mortality in the modern society. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. All doctors are unfortunately not equally skilled in every sub specialty and they are in many places a scarce resource. A system for automated medical diagnosis would enhance medical care and reduce costs. In this paper, a new approach based on coactive neuro-fuzzy inference system (CANFIS) was presented for prediction of heart disease. The proposed CANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach which is then integrated with genetic algorithm to diagnose the presence of the disease. The performances of the CANFIS model were evaluated in terms of training performances and classification accuracies and the results showed that the proposed CANFIS model has great potential in predicting the heart disease.
Abstract: With the hardware technology advancing, the cost of
storing is decreasing. Thus there is an urgent need for new techniques
and tools that can intelligently and automatically assist us in
transferring this data into useful knowledge. Different techniques of
data mining are developed which are helpful for handling these large
size databases [7]. Data mining is also finding its role in the field of
biotechnology. Pedigree means the associated ancestry of a crop
variety. Genetic diversity is the variation in the genetic composition
of individuals within or among species. Genetic diversity depends
upon the pedigree information of the varieties. Parents at lower
hierarchic levels have more weightage for predicting genetic
diversity as compared to the upper hierarchic levels. The weightage
decreases as the level increases. For crossbreeding, the two varieties
should be more and more genetically diverse so as to incorporate the
useful characters of the two varieties in the newly developed variety.
This paper discusses the searching and analyzing of different possible
pairs of varieties selected on the basis of morphological characters,
Climatic conditions and Nutrients so as to obtain the most optimal
pair that can produce the required crossbreed variety. An algorithm
was developed to determine the genetic diversity between the
selected wheat varieties. Cluster analysis technique is used for
retrieving the results.
Abstract: This paper proposes method of diagnosing ball screw
preload loss through the Hilbert-Huang Transform （HHT） and
Multiscale entropy （MSE） process. The proposed method can
diagnose ball screw preload loss through vibration signals when the
machine tool is in operation. Maximum dynamic preload of 2 %, 4 %,
and 6 % ball screws were predesigned, manufactured, and tested
experimentally. Signal patterns are discussed and revealed using
Empirical Mode Decomposition（EMD）with the Hilbert Spectrum.
Different preload features are extracted and discriminated using HHT.
The irregularity development of a ball screw with preload loss is
determined and abstracted using MSE based on complexity
perception. Experiment results show that the proposed method can
predict the status of ball screw preload loss. Smart sensing for the
health of the ball screw is also possible based on a comparative
evaluation of MSE by the signal processing and pattern matching of
EMD/HHT. This diagnosis method realizes the purposes of prognostic
effectiveness on knowing the preload loss and utilizing convenience.