Abstract: This paper summarizes the results of some experiments for finding the effective features for disambiguation of Turkish verbs. Word sense disambiguation is a current area of investigation in which verbs have the dominant role. Generally verbs have more senses than the other types of words in the average and detecting these features for verbs may lead to some improvements for other word types. In this paper we have considered only the syntactical features that can be obtained from the corpus and tested by using some famous machine learning algorithms.
Abstract: The purpose of the paper is to develop an informationcontrol environment for overall management and self-reconfiguration of the reconfigurable multifunctional machine tool for machining both rotation and prismatic parts and high concentration of different technological operations - turning, milling, drilling, grinding, etc. For the realization of this purpose on the basis of defined sub-processes for the implementation of the technological process, architecture of the information-search system for machine control is suggested. By using the object-oriented method, a structure and organization of the search system based on agents and manager with central control are developed. Thus conditions for identification of available information in DBs, self-reconfiguration of technological system and entire control of the reconfigurable multifunctional machine tool are created.
Abstract: While the explosive increase in information published
on the Web, researchers have to filter information when searching for
conference related information. To make it easier for users to search
related information, this paper uses Topic Maps and social information
to implement ontology since ontology can provide the formalisms and
knowledge structuring for comprehensive and transportable machine
understanding that digital information requires. Besides enhancing
information in Topic Maps, this paper proposes a method of
constructing research Topic Maps considering social information.
First, extract conference data from the web. Then extract conference
topics and the relationships between them through the proposed
method. Finally visualize it for users to search and browse. This paper
uses ontology, containing abundant of knowledge hierarchy structure,
to facilitate researchers getting useful search results. However, most
previous ontology construction methods didn-t take “people" into
account. So this paper also analyzes the social information which helps
researchers find the possibilities of cooperation/combination as well as
associations between research topics, and tries to offer better results.
Abstract: In this paper, multiobjective design of multi-machine Power System Stabilizers (PSSs) using Particle Swarm Optimization (PSO) is presented. The stabilizers are tuned to simultaneously shift the lightly damped and undamped electro-mechanical modes of all machines to a prescribed zone in the s-plane. A multiobjective problem is formulated to optimize a composite set of objective functions comprising the damping factor, and the damping ratio of the lightly damped electromechanical modes. The PSSs parameters tuning problem is converted to an optimization problem which is solved by PSO with the eigenvalue-based multiobjective function. The proposed PSO based PSSs is tested on a multimachine power system under different operating conditions and disturbances through eigenvalue analysis and some performance indices to illustrate its robust performance.
Abstract: In this paper presents a technique for developing the
computational efficiency in simulating double output induction
generators (DOIG) with two rotor circuits where stator transients are
to be included. Iterative decomposition is used to separate the flux–
Linkage equations into decoupled fast and slow subsystems, after
which the model order of the fast subsystems is reduced by
neglecting the heavily damped fast transients caused by the second
rotor circuit using integral manifolds theory. The two decoupled
subsystems along with the equation for the very slowly changing slip
constitute a three time-scale model for the machine which resulted in
increasing computational speed. Finally, the proposed method of
reduced order in this paper is compared with the other conventional
methods in linear and nonlinear modes and it is shown that this
method is better than the other methods regarding simulation
accuracy and speed.
Abstract: In the domain of machine vision, the
measurement of length is done using cameras where the
accuracy is directly proportional to the resolution of the
camera and inversely to the size of the object. Since most of
the pixels are wasted imaging the entire body as opposed to
just imaging the edges in a conventional system, a double
aperture system is constructed to focus on the edges to
measure at higher resolution. The paper discusses the
complexities and how they are mitigated to realize a practical
machine vision system.
Abstract: Least Development Countries (LDC) like
Bangladesh, whose 25% revenue earning is achieved from Textile
export, requires producing less defective textile for minimizing
production cost and time. Inspection processes done on these
industries are mostly manual and time consuming. To reduce error
on identifying fabric defects requires more automotive and
accurate inspection process. Considering this lacking, this research
implements a Textile Defect Recognizer which uses computer
vision methodology with the combination of multi-layer neural
networks to identify four classifications of textile defects. The
recognizer, suitable for LDC countries, identifies the fabric defects
within economical cost and produces less error prone inspection
system in real time. In order to generate input set for the neural
network, primarily the recognizer captures digital fabric images by
image acquisition device and converts the RGB images into binary
images by restoration process and local threshold techniques.
Later, the output of the processed image, the area of the faulty
portion, the number of objects of the image and the sharp factor of
the image, are feed backed as an input layer to the neural network
which uses back propagation algorithm to compute the weighted
factors and generates the desired classifications of defects as an
output.
Abstract: We report in this paper the procedure of a system of
automatic speech recognition based on techniques of the dynamic
programming. The technique of temporal retiming is a technique
used to synchronize between two forms to compare. We will see how
this technique is adapted to the field of the automatic speech
recognition. We will expose, in a first place, the theory of the
function of retiming which is used to compare and to adjust an
unknown form with a whole of forms of reference constituting the
vocabulary of the application. Then we will give, in the second place,
the various algorithms necessary to their implementation on machine.
The algorithms which we will present were tested on part of the
corpus of words in Arab language Arabdic-10 [4] and gave whole
satisfaction. These algorithms are effective insofar as we apply them
to the small ones or average vocabularies.
Abstract: Owning to the high-speed feed rate and ultra spindle
speed have been used in modern machine tools, the tool-path
generation plays a key role in the successful application of a
High-Speed Machining (HSM) system. Because of its importance in
both high-speed machining and tool-path generation, approximating a
contour by NURBS format is a potential function in CAD/CAM/CNC
systems. It is much more convenient to represent an ellipse by
parametric form than to connect points laboriously determined in a
CNC system. A new approximating method based on optimum
processes and NURBS curves of any degree to the ellipses is presented
in this study. Such operations can be the foundation of tool-radius
compensation interpolator of NURBS curves in CNC system. All
operating processes for a CAD tool is presented and demonstrated by
practical models.
Abstract: The main objective of this paper is to investigate the
enhancement of power system stability via coordinated tuning of
Power System Stabilizers (PSSs) in a multi-machine power system.
The design problem of the proposed controllers is formulated as an
optimization problem. Chaotic catfish particle swarm optimization
(C-Catfish PSO) algorithm is used to minimize the ITAE objective
function. The proposed algorithm is evaluated on a two-area, 4-
machines system. The robustness of the proposed algorithm is
verified on this system under different operating conditions and
applying a three-phase fault. The nonlinear time-domain simulation
results and some performance indices show the effectiveness of the
proposed controller in damping power system oscillations and this
novel optimization algorithm is compared with particle swarm
optimization (PSO).
Abstract: In recent years, real estate prediction or valuation has
been a topic of discussion in many developed countries. Improper
hype created by investors leads to fluctuating prices of real estate,
affecting many consumers to purchase their own homes. Therefore,
scholars from various countries have conducted research in real estate
valuation and prediction. With the back-propagation neural network
that has been popular in recent years and the orthogonal array in the
Taguchi method, this study aimed to find the optimal parameter
combination at different levels of orthogonal array after the system
presented different parameter combinations, so that the artificial
neural network obtained the most accurate results. The experimental
results also demonstrated that the method presented in the study had a
better result than traditional machine learning. Finally, it also showed
that the model proposed in this study had the optimal predictive effect,
and could significantly reduce the cost of time in simulation operation.
The best predictive results could be found with a fewer number of
experiments more efficiently. Thus users could predict a real estate
transaction price that is not far from the current actual prices.
Abstract: This paper presents an on-going research work on the
implementation of feature-based machining via macro programming.
Repetitive machining features such as holes, slots, pockets etc can
readily be encapsulated in macros. Each macro consists of methods
on how to machine the shape as defined by the feature. The macro
programming technique comprises of a main program and
subprograms. The main program allows user to select several
subprograms that contain features and define their important
parameters. With macros, complex machining routines can be
implemented easily and no post processor is required. A case study
on machining of a part that comprised of planar face, hole and pocket
features using the macro programming technique was carried out. It
is envisaged that the macro programming technique can be extended
to other feature-based machining fields such as the newly developed
STEP-NC domain.
Abstract: In the present study, a support vector machine (SVM) learning approach to character recognition is proposed. Simple
feature detectors, similar to those found in the human visual system, were used in the SVM classifier. Alphabetic characters were rotated
to 8 different angles and using the proposed cognitive model, all characters were recognized with 100% accuracy and specificity.
These same results were found in psychiatric studies of human character recognition.
Abstract: Manufacturing Industries face a crucial change as products and processes are required to, easily and efficiently, be reconfigurable and reusable. In order to stay competitive and flexible, situations also demand distribution of enterprises globally, which requires implementation of efficient communication strategies. A prototype system called the “Broadcaster" has been developed with an assumption that the control environment description has been engineered using the Component-based system paradigm. This prototype distributes information to a number of globally distributed partners via an adoption of the circular-based data processing mechanism. The work highlighted in this paper includes the implementation of this mechanism in the domain of the manufacturing industry. The proposed solution enables real-time remote propagation of machine information to a number of distributed supply chain client resources such as a HMI, VRML-based 3D views and remote client instances regardless of their distribution nature and/ or their mechanisms. This approach is presented together with a set of evaluation results. Authors- main concentration surrounds the reliability and the performance metric of the adopted approach. Performance evaluation is carried out in terms of the response times taken to process the data in this domain and compared with an alternative data processing implementation such as the linear queue mechanism. Based on the evaluation results obtained, authors justify the benefits achieved from this proposed implementation and highlight any further research work that is to be carried out.
Abstract: Rolling element bearings are widely used in industry,
especially where high load capacity is required. The diagnosis of
their conditions is essential matter for downtime reduction and saving
cost of maintenance. Therefore, an intensive analysis of frequency
spectrum of their faults must be carried out in order to determine the
main reason of the fault. This paper focus on a beating phenomena
observed in the waveform (time domain) of a cylindrical rolling
element bearing. The beating frequencies were not related to any
sources nearby the machine nor any other malfunctions (unbalance,
misalignment ...etc). More investigation on the spike energy and the
frequency spectrum indicated a problem with races of the bearing.
Multi-harmonics of the fundamental defects frequencies were
observed. Two of them were close to each other in magnitude those
were the source of the beating phenomena.
Abstract: This paper aims to improve a fine lapping process of
hard disk drive (HDD) lapping machines by removing materials from
each slider together with controlling the strip height (SH) variation to
minimum value. The standard deviation is the key parameter to
evaluate the strip height variation, hence it is minimized. In this
paper, a design of experiment (DOE) with factorial analysis by twoway
analysis of variance (ANOVA) is adopted to obtain a
statistically information. The statistics results reveal that initial stripe
height patterns affect the final SH variation. Therefore, initial SH
classification using a radial basis function neural network is
implemented to achieve the proportional gain prediction.
Abstract: This paper presents a novel method for prediction of
the mechanical behavior of proximal femur using the general
framework of the quantitative computed tomography (QCT)-based
finite element Analysis (FEA). A systematic imaging and modeling
procedure was developed for reliable correspondence between the
QCT-based FEA and the in-vitro mechanical testing. A speciallydesigned
holding frame was used to define and maintain a unique
geometrical reference system during the analysis and testing. The
QCT images were directly converted into voxel-based 3D finite
element models for linear and nonlinear analyses. The equivalent
plastic strain and the strain energy density measures were used to
identify the critical elements and predict the failure patterns. The
samples were destructively tested using a specially-designed gripping
fixture (with five degrees of freedom) mounted within a universal
mechanical testing machine. Very good agreements were found
between the experimental and the predicted failure patterns and the
associated load levels.
Abstract: This paper proposes a novel approach that combines statistical models and support vector machines. A hybrid scheme which appropriately incorporates the advantages of both the generative and discriminant model paradigms is described and evaluated. Support vector machines (SVMs) are trained to divide the whole speakers' space into small subsets of speakers within a hierarchical tree structure. During testing a speech token is assigned to its corresponding group and evaluation using gaussian mixture models (GMMs) is then processed. Experimental results show that the proposed method can significantly improve the performance of text independent speaker identification task. We report improvements of up to 50% reduction in identification error rate compared to the baseline statistical model.
Abstract: We have applied new accelerated algorithm for linear
discriminate analysis (LDA) in face recognition with support vector
machine. The new algorithm has the advantage of optimal selection
of the step size. The gradient descent method and new algorithm has
been implemented in software and evaluated on the Yale face
database B. The eigenfaces of these approaches have been used to
training a KNN. Recognition rate with new algorithm is compared
with gradient.
Abstract: Combining classifiers is a useful method for solving
complex problems in machine learning. The ECOC (Error Correcting
Output Codes) method has been widely used for designing combining
classifiers with an emphasis on the diversity of classifiers. In this
paper, in contrast to the standard ECOC approach in which individual
classifiers are chosen homogeneously, classifiers are selected
according to the complexity of the corresponding binary problem. We
use SATIMAGE database (containing 6 classes) for our experiments.
The recognition error rate in our proposed method is %10.37 which
indicates a considerable improvement in comparison with the
conventional ECOC and stack generalization methods.