Abstract: Severe heart failure is a common problem that has a significant effect on health expenditures in industrialized countries; moreover it reduces patient-s quality of life. However, current research usually focuses either on detailed modeling of the heart or on detailed modeling of the cardiovascular system. Thus, this paper aims to present a sophisticated model of the heart enhanced with an extensive model of the cardiovascular system. Special interest is on the pressure and flow values close to the heart since these values are critical to accurately diagnose causes of heart failure. The model is implemented in Dymola an object-oriented, physical modeling language. Results achieved with the novel model show overall feasibility of the approach. Moreover, results are illustrated and compared to other models. The novel model shows significant improvements.
Abstract: This paper presents a new Hybrid Fuzzy (HF) PID type controller based on Genetic Algorithms (GA-s) for solution of the Automatic generation Control (AGC) problem in a deregulated electricity environment. In order for a fuzzy rule based control system to perform well, the fuzzy sets must be carefully designed. A major problem plaguing the effective use of this method is the difficulty of accurately constructing the membership functions, because it is a computationally expensive combinatorial optimization problem. On the other hand, GAs is a technique that emulates biological evolutionary theories to solve complex optimization problems by using directed random searches to derive a set of optimal solutions. For this reason, the membership functions are tuned automatically using a modified GA-s based on the hill climbing method. The motivation for using the modified GA-s is to reduce fuzzy system effort and take large parametric uncertainties into account. The global optimum value is guaranteed using the proposed method and the speed of the algorithm-s convergence is extremely improved, too. This newly developed control strategy combines the advantage of GA-s and fuzzy system control techniques and leads to a flexible controller with simple stricture that is easy to implement. The proposed GA based HF (GAHF) controller is tested on a threearea deregulated power system under different operating conditions and contract variations. The results of the proposed GAHF controller are compared with those of Multi Stage Fuzzy (MSF) controller, robust mixed H2/H∞ and classical PID controllers through some performance indices to illustrate its robust performance for a wide range of system parameters and load changes.
Abstract: Accurate modeling of high speed RLC interconnects
has become a necessity to address signal integrity issues in current
VLSI design. To accurately model a dispersive system of interconnects
at higher frequencies; a full-wave analysis is required.
However, conventional circuit simulation of interconnects with full
wave models is extremely CPU expensive. We present an algorithm
for reducing large VLSI circuits to much smaller ones with similar
input-output behavior. A key feature of our method, called Frequency
Shift Technique, is that it is capable of reducing linear time-varying
systems. This enables it to capture frequency-translation and sampling
behavior, important in communication subsystems such as mixers,
RF components and switched-capacitor filters. Reduction is obtained
by projecting the original system described by linear differential
equations into a lower dimension. Experiments have been carried out
using Cadence Design Simulator cwhich indicates that the proposed
technique achieves more % reduction with less CPU time than the
other model order reduction techniques existing in literature. We
also present applications to RF circuit subsystems, obtaining size
reductions and evaluation speedups of orders of magnitude with
insignificant loss of accuracy.
Abstract: Iris-based biometric system is gaining its importance in several applications. However, processing of iris biometric is a challenging and time consuming task. Detection of iris part in an eye image poses a number of challenges such as, inferior image quality, occlusion of eyelids and eyelashes etc. Due to these problems it is not possible to achieve 100% accuracy rate in any iris-based biometric authentication systems. Further, iris detection is a computationally intensive task in the overall iris biometric processing. In this paper, we address these two problems and propose a technique to localize iris part efficiently and accurately. We propose scaling and color level transform followed by thresholding, finding pupil boundary points for pupil boundary detection and dilation, thresholding, vertical edge detection and removal of unnecessary edges present in the eye images for iris boundary detection. Scaling reduces the search space significantly and intensity level transform is helpful for image thresholding. Experimental results show that our approach is comparable with the existing approaches. Following our approach it is possible to detect iris part with 95-99% accuracy as substantiated by our experiments on CASIA Ver-3.0, ICE 2005, UBIRIS, Bath and MMU iris image databases.
Abstract: Series of experimental tests were conducted on a
section of a 660 kW wind turbine blade to measure the pressure
distribution of this model oscillating in plunging motion. In order to
minimize the amount of data required to predict aerodynamic loads
of the airfoil, a General Regression Neural Network, GRNN, was
trained using the measured experimental data. The network once
proved to be accurate enough, was used to predict the flow behavior
of the airfoil for the desired conditions.
Results showed that with using a few of the acquired data, the
trained neural network was able to predict accurate results with
minimal errors when compared with the corresponding measured
values. Therefore with employing this trained network the
aerodynamic coefficients of the plunging airfoil, are predicted
accurately at different oscillation frequencies, amplitudes, and angles
of attack; hence reducing the cost of tests while achieving acceptable
accuracy.
Abstract: Microstrip lines, widely used for good reason, are
broadband in frequency and provide circuits that are compact and
light in weight. They are generally economical to produce since they
are readily adaptable to hybrid and monolithic integrated circuit (IC)
fabrication technologies at RF and microwave frequencies. Although,
the existing EM simulation models used for the synthesis and
analysis of microstrip lines are reasonably accurate, they are
computationally intensive and time consuming. Neural networks
recently gained attention as fast and flexible vehicles to microwave
modeling, simulation and optimization. After learning and
abstracting from microwave data, through a process called training,
neural network models are used during microwave design to provide
instant answers to the task learned.This paper presents simple and
accurate ANN models for the synthesis and analysis of Microstrip
lines to more accurately compute the characteristic parameters and
the physical dimensions respectively for the required design
specifications.
Abstract: This paper presents the development of a wavelet
based algorithm, for distinguishing between magnetizing inrush
currents and power system fault currents, which is quite adequate,
reliable, fast and computationally efficient tool. The proposed
technique consists of a preprocessing unit based on discrete wavelet
transform (DWT) in combination with an artificial neural network
(ANN) for detecting and classifying fault currents. The DWT acts as
an extractor of distinctive features in the input signals at the relay
location. This information is then fed into an ANN for classifying
fault and magnetizing inrush conditions. A 220/55/55 V, 50Hz
laboratory transformer connected to a 380 V power system were
simulated using ATP-EMTP. The DWT was implemented by using
Matlab and Coiflet mother wavelet was used to analyze primary
currents and generate training data. The simulated results presented
clearly show that the proposed technique can accurately discriminate
between magnetizing inrush and fault currents in transformer
protection.
Abstract: For lack of the visualization of the ultrasonic detection
method of partial discharge (PD), the ultrasonic detection technology
combined with the X-ray visual detection method (UXV) is proposed.
The method can conduct qualitative analysis accurately and conduct
reliable positioning diagnosis to the internal insulation defects of
GIS, and while it could make up the blindness of the X-ray visual
detection method and improve the detection rate. In this paper, an
experimental model of GIS is used as the trial platform, a variety of
insulation defects are set inside the GIS cavity. With the proposed
method, the ultrasonic method is used to conduct the preliminary
detection, and then the X-ray visual detection is used to locate and
diagnose precisely. Therefore, the proposed UXV technology is
feasible and practical.
Abstract: In non destructive testing by radiography, a perfect
knowledge of the weld defect shape is an essential step to
appreciate the quality of the weld and make decision on its
acceptability or rejection. Because of the complex nature of the
considered images, and in order that the detected defect region
represents the most accurately possible the real defect, the choice
of thresholding methods must be done judiciously. In this paper,
performance criteria are used to conduct a comparative study of
four non parametric histogram thresholding methods for automatic
extraction of weld defect in radiographic images.
Abstract: This paper presents a wavelet transform and Support
Vector Machine (SVM) based algorithm for estimating fault location
on transmission lines. The Discrete wavelet transform (DWT) is used
for data pre-processing and this data are used for training and testing
SVM. Five types of mother wavelet are used for signal processing to
identify a suitable wavelet family that is more appropriate for use in
estimating fault location. The results demonstrated the ability of SVM
to generalize the situation from the provided patterns and to
accurately estimate the location of faults with varying fault resistance.
Abstract: According to dramatic growth of internet services, an easy and prompt service deployment has been important for internet service providers to successfully maintain time-to-market. Before global service deployment, they have to pay the big cost for service evaluation to make a decision of the proper system location, system scale, service delay and so on. But, intra-Lab evaluation tends to have big gaps in the measured data compared with the realistic situation, because it is very difficult to accurately expect the local service environment, network congestion, service delay, network bandwidth and other factors. Therefore, to resolve or ease the upper problems, we propose multiple cloud based GPES Broker system and use case that helps internet service providers to alleviate the above problems in beta release phase and to make a prompt decision for their service launching. By supporting more realistic and reliable evaluation information, the proposed GPES Broker system saves the service release cost and enables internet service provider to make a prompt decision about their service launching to various remote regions.
Abstract: When considering the development of constitutive
equations describing the behavior of materials under cyclic plastic
strains, different kinds of formulations can be adopted. The primary
intention of this study is to develop computer programming of
plasticity models to accurately predict the life of engineering
components. For this purpose, the energy or cyclic strain is computed
in multi-surface plasticity models in non-proportional loading and to
present their procedures and codes results.
Abstract: Until recently, researchers have developed various
tools and methodologies for effective clinical decision-making.
Among those decisions, chest pain diseases have been one of
important diagnostic issues especially in an emergency department. To
improve the ability of physicians in diagnosis, many researchers have
developed diagnosis intelligence by using machine learning and data
mining. However, most of the conventional methodologies have been
generally based on a single classifier for disease classification and
prediction, which shows moderate performance. This study utilizes an
ensemble strategy to combine multiple different classifiers to help
physicians diagnose chest pain diseases more accurately than ever.
Specifically the ensemble strategy is applied by using the integration
of decision trees, neural networks, and support vector machines. The
ensemble models are applied to real-world emergency data. This study
shows that the performance of the ensemble models is superior to each
of single classifiers.
Abstract: For decades, the defense business has been plagued by
not having a reliable, deterministic method to know when the Kalman
filter solution for passive ranging application is reliable for use by the
fighter pilot. This has made it hard to accurately assess when the
ranging solution can be used for situation awareness and weapons
use. To date, we have used ad hoc rules-of-thumb to assess when we
think the estimate of the Kalman filter standard deviation on range is
reliable. A reliable algorithm has been developed at BAE Systems
Electronics & Integrated Solutions that monitors the Kalman gain
matrix elements – and a patent is pending. The “settling" of the gain
matrix elements relates directly to when we can assess the time when
the passive ranging solution is within the 10 percent-of-truth value.
The focus of the paper is on surface-based passive ranging – but the
method is applicable to airborne targets as well.
Abstract: The dynamics of the Autonomous Underwater
Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate
accurately because of the variations of these coefficients with
different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain
the coupled nonlinear dynamic model of AUV as a black box. This black box has an input-output relationship based upon on-line
adaptive fuzzy model and adaptive neural fuzzy network (ANFN)
model techniques to overcome the uncertain external disturbance and
the difficulties of modelling the hydrodynamic forces of the AUVs instead of using the mathematical model with hydrodynamic parameters estimation. The models- parameters are adapted according
to the back propagation algorithm based upon the error between the
identified model and the actual output of the plant. The proposed
ANFN model adopts a functional link neural network (FLNN) as the
consequent part of the fuzzy rules. Thus, the consequent part of the
ANFN model is a nonlinear combination of input variables. Fuzzy
control system is applied to guide and control the AUV using both
adaptive models and mathematical model. Simulation results show
the superiority of the proposed adaptive neural fuzzy network
(ANFN) model in tracking of the behavior of the AUV accurately
even in the presence of noise and disturbance.
Abstract: Most fingerprint recognition techniques are based on minutiae matching and have been well studied. However, this technology still suffers from problems associated with the handling of poor quality impressions. One problem besetting fingerprint matching is distortion. Distortion changes both geometric position and orientation, and leads to difficulties in establishing a match among multiple impressions acquired from the same finger tip. Marking all the minutiae accurately as well as rejecting false minutiae is another issue still under research. Our work has combined many methods to build a minutia extractor and a minutia matcher. The combination of multiple methods comes from a wide investigation into research papers. Also some novel changes like segmentation using Morphological operations, improved thinning, false minutiae removal methods, minutia marking with special considering the triple branch counting, minutia unification by decomposing a branch into three terminations, and matching in the unified x-y coordinate system after a two-step transformation are used in the work.
Abstract: Although e-mail is the most efficient and popular communication method, unwanted and mass unsolicited e-mails, also called spam mail, endanger the existence of the mail system. This paper proposes a new algorithm called Dynamic Weighted Majority Concept Drift Detection (DWM-CDD) for content-based filtering. The design purposes of DWM-CDD are first to accurate the performance of the previously proposed algorithms, and second to speed up the time to construct the model. The results show that DWM-CDD can detect both sudden and gradual changes quickly and accurately. Moreover, the time needed for model construction is less than previously proposed algorithms.
Abstract: The purpose of this work is to present a method for
rigid registration of medical images using 1D binary projections
when a part of one of the two images is missing. We use 1D binary
projections and we adjust the projection limits according to the
reduced image in order to perform accurate registration. We use the
variance of the weighted ratio as a registration function which we
have shown is able to register 2D and 3D images more accurately and
robustly than mutual information methods. The function is computed
explicitly for n=5 Chebyshev points in a [-9,+9] interval and it is
approximated using Chebyshev polynomials for all other points. The
images used are MR scans of the head. We find that the method is
able to register the two images with average accuracy 0.3degrees for
rotations and 0.2 pixels for translations for a y dimension of 156 with
initial dimension 256. For y dimension 128/256 the accuracy
decreases to 0.7 degrees for rotations and 0.6 pixels for translations.
Abstract: The perfect operation of common Active Filters is depended on accuracy of identification system distortion. Also, using a suitable method in current injection and reactive power compensation, leads to increased filter performance. Due to this fact, this paper presents a method based on predictive current control theory in shunt active filter applications. The harmonics of the load current is identified by using o–d–q reference frame on load current and eliminating the DC part of d–q components. Then, the rest of these components deliver to predictive current controller as a Threephase reference current by using Park inverse transformation. System is modeled in discreet time domain. The proposed method has been tested using MATLAB model for a nonlinear load (with Total Harmonic Distortion=20%). The simulation results indicate that the proposed filter leads to flowing a sinusoidal current (THD=0.15%) through the source. In addition, the results show that the filter tracks the reference current accurately.
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.