Abstract: This paper presents a fuzzy logic controlled shunt
active power filter used to compensate for harmonic distortion in three-phase four-wire systems. The shunt active filter employs a
simple method for the calculation of the reference compensation current based of Fast Fourier Transform. This presented filter is able
to operate in both balanced and unbalanced load conditions. A fuzzy
logic based current controller strategy is used to regulate the filter current and hence ensure harmonic free supply current. The validity
of the presented approach in harmonic mitigation is verified via
simulation results of the proposed test system under different loading
conditions.
Abstract: Textures are replications, symmetries and
combinations of various basic patterns, usually with some random
variation one of the gray-level statistics. This article proposes a
new approach to Segment texture images. The proposed approach
proceeds in 2 stages. First, in this method, local texture information
of a pixel is obtained by fuzzy texture unit and global texture
information of an image is obtained by fuzzy texture spectrum.
The purpose of this paper is to demonstrate the usefulness of fuzzy
texture spectrum for texture Segmentation.
The 2nd Stage of the method is devoted to a decision process,
applying a global analysis followed by a fine segmentation,
which is only focused on ambiguous points. The above Proposed
approach was applied to brain image to identify the components
of brain in turn, used to locate the brain tumor and its Growth
rate.
Abstract: The objective of this study is to design an adaptive
neuro-fuzzy inference system (ANFIS) for estimation of surface
roughness in grinding process. The Used data have been generated
from experimental observations when the wheel has been dressed
using a rotary diamond disc dresser. The input parameters of model
are dressing speed ratio, dressing depth and dresser cross-feed rate
and output parameter is surface roughness. In the experimental
procedure the grinding conditions are constant and only the dressing
conditions are varied. The comparison of the predicted values and the
experimental data indicates that the ANFIS model has a better
performance with respect to back-propagation neural network
(BPNN) model which has been presented by the authors in previous
work for estimation of the surface roughness.
Abstract: Advances in clinical medical imaging have brought about the routine production of vast numbers of medical images that need to be analyzed. As a result an enormous amount of computer vision research effort has been targeted at achieving automated medical image analysis. Computed Tomography (CT) is highly accurate for diagnosing liver tumors. This study aimed to evaluate the potential role of the wavelet and the neural network in the differential diagnosis of liver tumors in CT images. The tumors considered in this study are hepatocellular carcinoma, cholangio carcinoma, hemangeoma and hepatoadenoma. Each suspicious tumor region was automatically extracted from the CT abdominal images and the textural information obtained was used to train the Probabilistic Neural Network (PNN) to classify the tumors. Results obtained were evaluated with the help of radiologists. The system differentiates the tumor with relatively high accuracy and is therefore clinically useful.
Abstract: In this paper, the order, size and degree of the nodes
of the isomorphic fuzzy graphs are discussed. Isomorphism between
fuzzy graphs is proved to be an equivalence relation. Some properties
of self complementary and self weak complementary fuzzy graphs
are discussed.
Abstract: In this paper the concept of the cosets of an anti Lfuzzy
normal subgroup of a group is given. Furthermore, the group
G/A of cosets of an anti L-fuzzy normal subgroup A of a group
G is shown to be isomorphic to a factor group of G in a natural
way. Finally, we prove that if f : G1 -→ G2 is an epimorphism of
groups, then there is a one-to-one order-preserving correspondence
between the anti L-fuzzy normal subgroups of G2 and those of G1
which are constant on the kernel of f.
Abstract: This paper develops a quality estimation method with
the application of fuzzy hierarchical clustering. Quality estimation is
essential to quality control and quality improvement as a precise
estimation can promote a right decision-making in order to help
better quality control. Normally the quality of finished products in
manufacturing system can be differentiated by quality standards. In
the real life situation, the collected data may be vague which is not
easy to be classified and they are usually represented in term of fuzzy
number. To estimate the quality of product presented by fuzzy
number is not easy. In this research, the trapezoidal fuzzy numbers
are collected in manufacturing process and classify the collected data
into different clusters so as to get the estimation. Since normal
hierarchical clustering methods can only be applied for real numbers,
fuzzy hierarchical clustering is selected to handle this problem based
on quality standards.
Abstract: Intrusion Detection Systems are increasingly a key
part of systems defense. Various approaches to Intrusion Detection
are currently being used, but they are relatively ineffective. Artificial
Intelligence plays a driving role in security services. This paper
proposes a dynamic model Intelligent Intrusion Detection System,
based on specific AI approach for intrusion detection. The
techniques that are being investigated includes neural networks and
fuzzy logic with network profiling, that uses simple data mining
techniques to process the network data. The proposed system is a
hybrid system that combines anomaly, misuse and host based
detection. Simple Fuzzy rules allow us to construct if-then rules that
reflect common ways of describing security attacks. For host based
intrusion detection we use neural-networks along with self
organizing maps. Suspicious intrusions can be traced back to its
original source path and any traffic from that particular source will
be redirected back to them in future. Both network traffic and system
audit data are used as inputs for both.
Abstract: This paper is described one of the intelligent control method in Autonomous systems, which is called fuzzy control to correct the three wheel omnidirectional robot movement while it make mistake to catch the target. Fuzzy logic is especially advantageous for problems that can not be easily represented by mathematical modeling because data is either unavailable, incomplete or the process is too complex. Such systems can be easily up grated by adding new rules to improve performance or add new features. In many cases , fuzzy control can be used to improve existing traditional controller systems by adding an extra layer of intelligence to the current control method. The fuzzy controller designed here is more accurate and flexible than the traditional controllers. The project is done at MRL middle size soccer robot team.
Abstract: This paper examines the problem of designing robust H controllers for for HIV/AIDS infection system with dual drug dosages described by a Takagi-Sugeno (S) fuzzy model. Based on a linear matrix inequality (LMI) approach, we develop an H controller which guarantees the L2-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value for the system. A sufficient condition of the controller for this system is given in term of Linear Matrix Inequalities (LMIs). The effectiveness of the proposed controller design methodology is finally demonstrated through simulation results. It has been shown that the anti-HIV vaccines are critically important in reducing the infected cells.
Abstract: This paper proposes a new decision making structure
to determine the appropriate product delivery strategy for different products in a manufacturing system among make-to-stock, make-toorder,
and hybrid strategy. Given product delivery strategies for all products in the manufacturing system, the position of the Order
Penetrating Point (OPP) can be located regarding the delivery strategies among which location of OPP in hybrid strategy is a
cumbersome task. In this regard, we employ analytic network process, because there are varieties of interrelated driving factors
involved in choosing the right location. Moreover, the proposed structure is augmented with fuzzy sets theory in order to cope with
the uncertainty of judgments. Finally, applicability of the proposed structure is proven in practice through a real industrial case company.
The numerical results demonstrate the efficiency of the proposed decision making structure in order partitioning and OPP location.
Abstract: A lot of computer-based methods have been developed
to assess the evacuation capability (EC) of high-rise buildings.
Because softwares are time-consuming and not proper for on scene
applications, we adopted two methods, fuzzy analytic hierarchy
process (FAHP) and technique for order preference by similarity to an
ideal solution (TOPSIS), for EC assessment of a high-rise building in
Jinan. The EC scores obtained with the two methods and the
evacuation time acquired with Pathfinder 2009 for floors 47-60 of the
building were compared with each other. The results show that FAHP
performs better than TOPSIS for EC assessment of high-rise buildings,
especially in the aspect of dealing with the effect of occupant type and
distance to exit on EC, tackling complex problem with multi-level
structure of criteria, and requiring less amount of computation.
However, both FAHP and TOPSIS failed to appropriately handle the
situation where the exit width changes while occupants are few.
Abstract: A novel low-cost impedance control structure is
proposed for monitoring the contact force between end-effector and
environment without installing an expensive force/torque sensor.
Theoretically, the end-effector contact force can be estimated from the
superposition of each joint control torque. There have a nonlinear
matrix mapping function between each joint motor control input and
end-effector actuating force/torques vector. This new force control
structure can be implemented based on this estimated mapping matrix.
First, the robot end-effector is manipulated to specified positions, then
the force controller is actuated based on the hall sensor current
feedback of each joint motor. The model-free fuzzy sliding mode
control (FSMC) strategy is employed to design the position and force
controllers, respectively. All the hardware circuits and software
control programs are designed on an Altera Nios II embedded
development kit to constitute an embedded system structure for a
retrofitted Mitsubishi 5 DOF robot. Experimental results show that PI
and FSMC force control algorithms can achieve reasonable contact
force monitoring objective based on this hardware control structure.
Abstract: Because of the low maintenance and robustness induction motors have many applications in the industries. The speed control of induction motor is more important to achieve maximum torque and efficiency. Various speed control techniques like, Direct Torque Control, Sensorless Vector Control and Field Oriented Control are discussed in this paper. Soft computing technique – Fuzzy logic is applied in this paper for the speed control of induction motor to achieve maximum torque with minimum loss. The fuzzy logic controller is implemented using the Field Oriented Control technique as it provides better control of motor torque with high dynamic performance. The motor model is designed and membership functions are chosen according to the parameters of the motor model. The simulated design is tested using various tool boxes in MATLAB. The result concludes that the efficiency and reliability of the proposed speed controller is good.
Abstract: Time series forecasting is an important and widely
popular topic in the research of system modeling. This paper
describes how to use the hybrid PSO-RLSE neuro-fuzzy learning
approach to the problem of time series forecasting. The PSO
algorithm is used to update the premise parameters of the
proposed prediction system, and the RLSE is used to update the
consequence parameters. Thanks to the hybrid learning (HL)
approach for the neuro-fuzzy system, the prediction performance
is excellent and the speed of learning convergence is much faster
than other compared approaches. In the experiments, we use the
well-known Mackey-Glass chaos time series. According to the
experimental results, the prediction performance and accuracy in
time series forecasting by the proposed approach is much better
than other compared approaches, as shown in Table IV. Excellent
prediction performance by the proposed approach has been
observed.
Abstract: Safer driver behavior promoting is the main goal of this paper. It is a fact that drivers behavior is relatively safer when being monitored. Thus, in this paper, we propose a monitoring system to report specific driving event as well as the potentially aggressive events for estimation of the driving performance. Our driving monitoring system is composed of two parts. The first part is the in-vehicle embedded system which is composed of a GPS receiver, a two-axis accelerometer, radar sensor, OBD interface, and GPRS modem. The design considerations that led to this architecture is described in this paper. The second part is a web server where an adaptive hierarchical fuzzy system is proposed to classify the driving performance based on the data that is sent by the in-vehicle embedded system and the data that is provided by the geographical information system (GIS). Our system is robust, inexpensive and small enough to fit inside a vehicle without distracting the driver.
Abstract: This paper investigates the effect of product substitution in the single-period 'newsboy-type' problem in a fuzzy environment. It is supposed that the single-period problem operates under uncertainty in customer demand, which is described by imprecise terms and modelled by fuzzy sets. To perform this analysis, we consider the fuzzy model for two-item with upward substitution. This upward substitutability is reasonable when the products can be stored according to certain attribute levels such as quality, brand or package size. We show that the explicit consideration of this substitution opportunity increase the average expected profit. Computational study is performed to observe the benefits of product's substitution.
Abstract: In this paper, a neural network tuned fuzzy controller
is proposed for controlling Multi-Input Multi-Output (MIMO)
systems. For the convenience of analysis, the structure of MIMO
fuzzy controller is divided into single input single-output (SISO)
controllers for controlling each degree of freedom. Secondly,
according to the characteristics of the system-s dynamics coupling, an
appropriate coupling fuzzy controller is incorporated to improve the
performance. The simulation analysis on a two-level mass–spring
MIMO vibration system is carried out and results show the
effectiveness of the proposed fuzzy controller. The performance
though improved, the computational time and memory used is
comparatively higher, because it has four fuzzy reasoning blocks and
number may increase in case of other MIMO system. Then a fuzzy
neural network is designed from a set of input-output training data to
reduce the computing burden during implementation. This control
strategy can not only simplify the implementation problem of fuzzy
control, but also reduce computational time and consume less
memory.
Abstract: The purpose of determining impact significance is to
place value on impacts. Environmental impact assessment review is a
process that judges whether impact significance is acceptable or not in
accordance with the scientific facts regarding environmental,
ecological and socio-economical impacts described in environmental
impact statements (EIS) or environmental impact assessment reports
(EIAR). The first aim of this paper is to summarize the criteria of
significance evaluation from the past review results and accordingly
utilize fuzzy logic to incorporate these criteria into scientific facts. The
second aim is to employ data mining technique to construct an EIS or
EIAR prediction model for reviewing results which can assist
developers to prepare and revise better environmental management
plans in advance. The validity of the previous prediction model
proposed by authors in 2009 is 92.7%. The enhanced validity in this
study can attain 100.0%.
Abstract: In many sensor network applications, sensor nodes are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the node's cryptographic keys. False sensing report can be injected through compromised nodes, which can lead to not only false alarms but also the depletion of limited energy resource in battery powered networks. Ye et al. proposed a statistical en-route filtering scheme (SEF) to detect such false reports during the forwarding process. In this scheme, the choice of a security threshold value is important since it trades off detection power and overhead. In this paper, we propose a fuzzy logic for determining a security threshold value in the SEF based sensor networks. The fuzzy logic determines a security threshold by considering the number of partitions in a global key pool, the number of compromised partitions, and the energy level of nodes. The fuzzy based threshold value can conserve energy, while it provides sufficient detection power.