Abstract: The primary objective of this paper was to construct a
“kinematic parameter-independent modeling of three-axis machine
tools for geometric error measurement" technique. Improving the
accuracy of the geometric error for three-axis machine tools is one of
the machine tools- core techniques. This paper first applied the
traditional method of HTM to deduce the geometric error model for
three-axis machine tools. This geometric error model was related to the
three-axis kinematic parameters where the overall errors was relative
to the machine reference coordinate system. Given that the
measurement of the linear axis in this model should be on the ideal
motion axis, there were practical difficulties. Through a measurement
method consolidating translational errors and rotational errors in the
geometric error model, we simplified the three-axis geometric error
model to a kinematic parameter-independent model. Finally, based on
the new measurement method corresponding to this error model, we
established a truly practical and more accurate error measuring
technique for three-axis machine tools.
Abstract: Fundamental sensor-motor couplings form the backbone
of most mobile robot control tasks, and often need to be implemented
fast, efficiently and nevertheless reliably. Machine learning
techniques are therefore often used to obtain the desired sensor-motor
competences.
In this paper we present an alternative to established machine
learning methods such as artificial neural networks, that is very fast,
easy to implement, and has the distinct advantage that it generates
transparent, analysable sensor-motor couplings: system identification
through nonlinear polynomial mapping.
This work, which is part of the RobotMODIC project at the
universities of Essex and Sheffield, aims to develop a theoretical understanding
of the interaction between the robot and its environment.
One of the purposes of this research is to enable the principled design
of robot control programs.
As a first step towards this aim we model the behaviour of the
robot, as this emerges from its interaction with the environment, with
the NARMAX modelling method (Nonlinear, Auto-Regressive, Moving
Average models with eXogenous inputs). This method produces
explicit polynomial functions that can be subsequently analysed using
established mathematical methods.
In this paper we demonstrate the fidelity of the obtained NARMAX
models in the challenging task of robot route learning; we present a
set of experiments in which a Magellan Pro mobile robot was taught
to follow four different routes, always using the same mechanism to
obtain the required control law.
Abstract: Concerning the measurement of friction properties of
textiles and fabrics using Kawabata Evaluation System (KES), whose
output is constrained to the surface friction factor of fabric, and no
other data would be generated; this research has been conducted to
gain information about surface roughness regarding its surface
friction factor. To assess roughness properties of light nonwovens, a
3-dimensional model of a surface has been simulated with regular
sinuous waves through it as an ideal surface. A new factor was
defined, namely Surface Roughness Factor, through comparing
roughness properties of simulated surface and real specimens. The
relation between the proposed factor and friction factor of specimens
has been analyzed by regression, and results showed a meaningful
correlation between them. It can be inferred that the new presented
factor can be used as an acceptable criterion for evaluating the
roughness properties of light nonwoven fabrics.
Abstract: Many natural language expressions are ambiguous, and
need to draw on other sources of information to be interpreted.
Interpretation of the e word تعاون to be considered as a noun or a verb
depends on the presence of contextual cues. To interpret words we
need to be able to discriminate between different usages. This paper
proposes a hybrid of based- rules and a machine learning method for
tagging Arabic words. The particularity of Arabic word that may be
composed of stem, plus affixes and clitics, a small number of rules
dominate the performance (affixes include inflexional markers for
tense, gender and number/ clitics include some prepositions,
conjunctions and others). Tagging is closely related to the notion of
word class used in syntax. This method is based firstly on rules (that
considered the post-position, ending of a word, and patterns), and
then the anomaly are corrected by adopting a memory-based learning
method (MBL). The memory_based learning is an efficient method to
integrate various sources of information, and handling exceptional
data in natural language processing tasks. Secondly checking the
exceptional cases of rules and more information is made available to
the learner for treating those exceptional cases. To evaluate the
proposed method a number of experiments has been run, and in
order, to improve the importance of the various information in
learning.
Abstract: This paper presents a novel genetic algorithm, termed
the Optimum Individual Monogenetic Algorithm (OIMGA) and
describes its hardware implementation. As the monogenetic strategy
retains only the optimum individual, the memory requirement is
dramatically reduced and no crossover circuitry is needed, thereby
ensuring the requisite silicon area is kept to a minimum.
Consequently, depending on application requirements, OIMGA
allows the investigation of solutions that warrant either larger GA
populations or individuals of greater length. The results given in this
paper demonstrate that both the performance of OIMGA and its
convergence time are superior to those of existing hardware GA
implementations. Local convergence is achieved in OIMGA by
retaining elite individuals, while population diversity is ensured by
continually searching for the best individuals in fresh regions of the
search space.
Abstract: In this paper the use of sequential machines for recognizing actions taken by the objects detected by a general tracking algorithm is proposed. The system may deal with the uncertainty inherent in medium-level vision data. For this purpose, fuzzification of input data is performed. Besides, this transformation allows to manage data independently of the tracking application selected and enables adding characteristics of the analyzed scenario. The representation of actions by means of an automaton and the generation of the input symbols for finite automaton depending on the object and action compared are described. The output of the comparison process between an object and an action is a numerical value that represents the membership of the object to the action. This value is computed depending on how similar the object and the action are. The work concludes with the application of the proposed technique to identify the behavior of vehicles in road traffic scenes.
Abstract: One of the best ways for achievement of conventional
vehicle changing to hybrid case is trustworthy simulation result and
using of driving realities. For this object, in this paper, at first sevendegree-
of-freedom dynamical model of vehicle will be shown. Then
by using of statically model of engine, gear box, clutch, differential,
electrical machine and battery, the hybrid automobile modeling will
be down and forward simulation of vehicle for pedals to wheels
power transformation will be obtained. Then by design of a fuzzy
controller and using the proper rule base, fuel economy and
regenerative braking will be marked. Finally a series of
MATLAB/SIMULINK simulation results will be proved the
effectiveness of proposed structure.
Abstract: As the web continues to grow exponentially, the idea
of crawling the entire web on a regular basis becomes less and less
feasible, so the need to include information on specific domain,
domain-specific search engines was proposed. As more information
becomes available on the World Wide Web, it becomes more difficult
to provide effective search tools for information access. Today,
people access web information through two main kinds of search
interfaces: Browsers (clicking and following hyperlinks) and Query
Engines (queries in the form of a set of keywords showing the topic
of interest) [2]. Better support is needed for expressing one's
information need and returning high quality search results by web
search tools. There appears to be a need for systems that do reasoning
under uncertainty and are flexible enough to recover from the
contradictions, inconsistencies, and irregularities that such reasoning
involves. In a multi-view problem, the features of the domain can be
partitioned into disjoint subsets (views) that are sufficient to learn the
target concept. Semi-supervised, multi-view algorithms, which
reduce the amount of labeled data required for learning, rely on the
assumptions that the views are compatible and uncorrelated. This
paper describes the use of semi-structured machine learning approach
with Active learning for the “Domain Specific Search Engines". A
domain-specific search engine is “An information access system that
allows access to all the information on the web that is relevant to a
particular domain. The proposed work shows that with the help of
this approach relevant data can be extracted with the minimum
queries fired by the user. It requires small number of labeled data and
pool of unlabelled data on which the learning algorithm is applied to
extract the required data.
Abstract: In the present article, a new method has been developed to enhance the application of equipment monitoring, which in turn results in improving condition-based maintenance economic impact in an automobile parts manufacturing factory. This study also describes how an effective software with a simple database can be utilized to achieve cost-effective improvements in maintenance performance. The most important results of this project are indicated here: 1. 63% reduction in direct and indirect maintenance costs. 2. Creating a proper database to analyse failures. 3. Creating a method to control system performance and develop it to similar systems. 4. Designing a software to analyse database and consequently create technical knowledge to face unusual condition of the system. Moreover, the results of this study have shown that the concept and philosophy of maintenance has not been understood in most Iranian industries. Thus, more investment is strongly required to improve maintenance conditions.
Abstract: Digital news with a variety topics is abundant on the
internet. The problem is to classify news based on its appropriate
category to facilitate user to find relevant news rapidly. Classifier
engine is used to split any news automatically into the respective
category. This research employs Support Vector Machine (SVM) to
classify Indonesian news. SVM is a robust method to classify
binary classes. The core processing of SVM is in the formation of an
optimum separating plane to separate the different classes. For
multiclass problem, a mechanism called one against one is used to
combine the binary classification result. Documents were taken
from the Indonesian digital news site, www.kompas.com. The
experiment showed a promising result with the accuracy rate of 85%.
This system is feasible to be implemented on Indonesian news
classification.
Abstract: Reliability assessment and risk analysis of rotating
machine rotors in various overload and malfunction situations
present challenge to engineers and operators. In this paper a new
analytical method for evaluation of rotor under large deformation is
addressed. Model is presented in general form to include also
composite rotors. Presented simulation procedure is based on
variational work method and has capability to account for geometric
nonlinearity, large displacement, nonlinear support effect and rotor
contacting other machine components. New shape functions are
presented which capable to predict accurate nonlinear profile of
rotor. The closed form solutions for various operating and
malfunction situations are expressed. Analytical simulation results
are discussed
Abstract: Pattern matching based on regular tree grammars have been widely used in many areas of computer science. In this paper, we propose a pattern matcher within the framework of code generation, based on a generic and a formalized approach. According to this approach, parsers for regular tree grammars are adapted to a general pattern matching solution, rather than adapting the pattern matching according to their parsing behavior. Hence, we first formalize the construction of the pattern matches respective to input trees drawn from a regular tree grammar in a form of the so-called match trees. Then, we adopt a recently developed generic parser and tightly couple its parsing behavior with such construction. In addition to its generality, the resulting pattern matcher is characterized by its soundness and efficient implementation. This is demonstrated by the proposed theory and by the derived algorithms for its implementation. A comparison with similar and well-known approaches, such as the ones based on tree automata and LR parsers, has shown that our pattern matcher can be applied to a broader class of grammars, and achieves better approximation of pattern matches in one pass. Furthermore, its use as a machine code selector is characterized by a minimized overhead, due to the balanced distribution of the cost computations into static ones, during parser generation time, and into dynamic ones, during parsing time.
Abstract: The machining performance is determined by the
frequency characteristics of the machine-tool structure and the
dynamics of the cutting process. Therefore, the prediction of dynamic
vibration behavior of spindle tool system is of great importance for the
design of a machine tool capable of high-precision and high-speed
machining. The aim of this study is to develop a finite element model
to predict the dynamic characteristics of milling machine tool and
hence evaluate the influence of the preload of the spindle bearings. To
this purpose, a three dimensional spindle bearing model of a high
speed engraving spindle tool was created. In this model, the rolling
interfaces with contact stiffness defined by Harris model were used to
simulate the spindle bearing components. Then a full finite element
model of a vertical milling machine was established by coupling the
spindle tool unit with the machine frame structure. Using this model,
the vibration mode that had a dominant influence on the dynamic
stiffness was determined. The results of the finite element simulations
reveal that spindle bearing with different preloads greatly affect the
dynamic behavior of the spindle tool unit and hence the dynamic
responses of the vertical column milling system. These results were
validated by performing vibration on the individual spindle tool unit
and the milling machine prototype, respectively. We conclude that
preload of the spindle bearings is an important component affecting
the dynamic characteristics and machining performance of the entire
vertical column structure of the milling machine.
Abstract: This paper presents a speed fuzzy sliding mode
controller for a vector controlled induction machine (IM) fed by a
voltage source inverter (PWM).
The sliding mode based fuzzy control method is developed to
achieve fast response, a best disturbance rejection and to maintain a
good decoupling.
The problem with sliding mode control is that there is high
frequency switching around the sliding mode surface. The FSMC is
the combination of the robustness of Sliding Mode Control (SMC)
and the smoothness of Fuzzy Logic (FL). To reduce the torque
fluctuations (chattering), the sign function used in the conventional
SMC is substituted with a fuzzy logic algorithm.
The proposed algorithm was simulated by Matlab/Simulink
software and simulation results show that the performance of the
control scheme is robust and the chattering problem is solved.
Abstract: Keystroke authentication is a new access control system
to identify legitimate users via their typing behavior. In this paper,
machine learning techniques are adapted for keystroke authentication.
Seven learning methods are used to build models to differentiate user
keystroke patterns. The selected classification methods are Decision
Tree, Naive Bayesian, Instance Based Learning, Decision Table, One
Rule, Random Tree and K-star. Among these methods, three of them
are studied in more details. The results show that machine learning
is a feasible alternative for keystroke authentication. Compared to
the conventional Nearest Neighbour method in the recent research,
learning methods especially Decision Tree can be more accurate. In
addition, the experiment results reveal that 3-Grams is more accurate
than 2-Grams and 4-Grams for feature extraction. Also, combination
of attributes tend to result higher accuracy.
Abstract: The objective of this paper is to characterize the spontaneous Electroencephalogram (EEG) signals of four different motor imagery tasks and to show hereby a possible solution for the present binary communication between the brain and a machine ora Brain-Computer Interface (BCI). The processing technique used in this paper was the fractal analysis evaluated by the Critical Exponent Method (CEM). The EEG signal was registered in 5 healthy subjects,sampling 15 measuring channels at 1024 Hz.Each channel was preprocessed by the Laplacian space ltering so as to reduce the space blur and therefore increase the spaceresolution. The EEG of each channel was segmented and its Fractaldimension (FD) calculated. The FD was evaluated in the time interval corresponding to the motor imagery and averaged out for all the subjects (each channel). In order to characterize the FD distribution,the linear regression curves of FD over the electrodes position were applied. The differences FD between the proposed mental tasks are quantied and evaluated for each experimental subject. The obtained results of the proposed method are a substantial fractal dimension in the EEG signal of motor imagery tasks and can be considerably utilized as the multiple-states BCI applications.
Abstract: Increasing the demand for effectively use of the
production facility requires the tools for sharing the manufacturing
facility through remote operation of the machining process. This
research introduces the methodology of machining technology for
direct remote operation of networked milling machine. The
integrated tools with virtual simulation, remote desktop protocol and
Setup Free Attachment for remote operation of milling process are
proposed. Accessing and monitoring of machining operation is
performed by remote desktop interface and 3D virtual simulations.
Capability of remote operation is supported by an auto setup
attachment with a reconfigurable pin type setup free technology
installed on the table of CNC milling machine to perform unattended
machining process. The system is designed using a computer server
and connected to a PC based controlled CNC machine for real time
monitoring. A client will access the server through internet
communication and virtually simulate the machine activity. The
result has been presented that combination between real time virtual
simulation and remote desktop tool is enabling to operate all machine
tool functions and as well as workpiece setup..
Abstract: This paper introduces a novel approach to estimate the
clique potentials of Gibbs Markov random field (GMRF) models
using the Support Vector Machines (SVM) algorithm and the Mean
Field (MF) theory. The proposed approach is based on modeling the
potential function associated with each clique shape of the GMRF
model as a Gaussian-shaped kernel. In turn, the energy function of
the GMRF will be in the form of a weighted sum of Gaussian
kernels. This formulation of the GMRF model urges the use of the
SVM with the Mean Field theory applied for its learning for
estimating the energy function. The approach has been tested on
synthetic texture images and is shown to provide satisfactory results
in retrieving the synthesizing parameters.
Abstract: This paper presents an algorithm which extends the rapidly-exploring random tree (RRT) framework to deal with change of the task environments. This algorithm called the Retrieval RRT Strategy (RRS) combines a support vector machine (SVM) and RRT and plans the robot motion in the presence of the change of the surrounding environment. This algorithm consists of two levels. At the first level, the SVM is built and selects a proper path from the bank of RRTs for a given environment. At the second level, a real path is planned by the RRT planners for the given environment. The suggested method is applied to the control of KUKA™,, a commercial 6 DOF robot manipulator, and its feasibility and efficiency are demonstrated via the cosimulatation of MatLab™, and RecurDyn™,.
Abstract: Automotive engine air-ratio plays an important role of
emissions and fuel consumption reduction while maintains
satisfactory engine power among all of the engine control variables. In
order to effectively control the air-ratio, this paper presents a model
predictive fuzzy control algorithm based on online least-squares
support vector machines prediction model and fuzzy logic optimizer.
The proposed control algorithm was also implemented on a real car for
testing and the results are highly satisfactory. Experimental results
show that the proposed control algorithm can regulate the engine
air-ratio to the stoichiometric value, 1.0, under external disturbance
with less than 5% tolerance.