Abstract: Electronic nose (array of chemical sensors) are widely
used in food industry and pollution control. Also it could be used to
locate or detect the direction of the source of emission odors. Usually
this task is performed by electronic nose (ENose) cooperated with
mobile vehicles, but when a source is instantaneous or surrounding is
hard for vehicles to reach, problem occurs. Thus a method for
stationary ENose to detect the direction of the source and locate the
source will be required. A novel method which uses the ratio between
the responses of different sensors as a discriminant to determine the
direction of source in natural wind surroundings is presented in this
paper. The result shows that the method is accurate and easily to be
implemented. This method could be also used in movably, as an
optimized algorithm for robot tracking source location.
Abstract: This work deals with the design of the robotic assembly
system for the roller clamps. The task is characterized by high speed,
high yield and safety engagement. This paper describes the design of
different parts of an automated high speed machine to assemble the
parts of roller clamps. The roller clamp robotic assembly system
performs various processes in the assembly line which include clamp
body and roller feeding, inserting the roller into the clamp body, and
dividing the rejected clamp and successfully assembled clamp into
their own tray. The electrical/electronics design of the machine is
discussed. The target is to design a cost effective, minimum
maintenance and high speed machine for the industry applications.
Abstract: The overriding goal of software engineering is to
provide a high quality system, application or a product. To achieve
this goal, software engineers must apply effective methods coupled
with modern tools within the context of a mature software process
[2]. In addition, it is also must to assure that high quality is realized.
Although many quality measures can be collected at the project
levels, the important measures are errors and defects. Deriving a
quality measure for reusable components has proven to be
challenging task now a days. The results obtained from the study are
based on the empirical evidence of reuse practices, as emerged from
the analysis of industrial projects. Both large and small companies,
working in a variety of business domains, and using object-oriented
and procedural development approaches contributed towards this
study. This paper proposes a quality metric that provides benefit at
both project and process level, namely defect removal efficiency
(DRE).
Abstract: The objective of the paper is twofold. First, to develop a
formal framework for planning for mobile agents. A logical language
based on a temporal logic is proposed that can express a type of
tasks which often arise in network management. Second, to design a
planning algorithm for such tasks. The aim of this paper is to study
the importance of finding plans for mobile agents. Although there
has been a lot of research in mobile agents, not much work has been
done to incorporate planning ideas for such agents. This paper makes
an attempt in this direction. A theoretical study of finding plans for
mobile agents is undertaken. A planning algorithm (based on the
paradigm of mobile computing) is proposed and its space, time, and
communication complexity is analyzed. The algorithm is illustrated
by working out an example in detail.
Abstract: An implementation of a design for a game based
virtual learning environment is described. The game is developed for a course in analogue electronics, and the topic is the design of a
power supply. This task can be solved in a number of different ways, with certain constraints, giving the students a certain amount of
freedom, although the game is designed not to facilitate trial-and error approach. The use of storytelling and a virtual gaming environment provides the student with the learning material in a
MMORPG environment. The game is tested on a group of second
year electrical engineering students with good results.
Abstract: Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, a density based clustering algorithm (DCBRD) is presented, relying on a knowledge acquired from the data by dividing the data space into overlapped regions. The proposed algorithm discovers arbitrary shaped clusters, requires no input parameters and uses the same definitions of DBSCAN algorithm. We performed an experimental evaluation of the effectiveness and efficiency of it, and compared this results with that of DBSCAN. The results of our experiments demonstrate that the proposed algorithm is significantly efficient in discovering clusters of arbitrary shape and size.
Abstract: Intelligence tests are series of tasks designed to measure the capacity to make abstractions, to learn, and to deal with novel situations. Testing of the visual abilities of the shape understanding system (SUS) is performed based on the visual intelligence tests. In this paper the progressive matrices tests are formulated as tasks given to SUS. These tests require good visual problem solving abilities of the human subject. SUS solves these tests by performing complex visual reasoning transforming the visual forms (tests) into the string forms. The experiment proved that the proposed method, which is part of the SUS visual understanding abilities, can solve a test that is very difficult for human subject.
Abstract: The Automatic Speech Recognition (ASR) applied to
Arabic language is a challenging task. This is mainly related to the
language specificities which make the researchers facing multiple
difficulties such as the insufficient linguistic resources and the very
limited number of available transcribed Arabic speech corpora. In
this paper, we are interested in the development of a HMM-based
ASR system for Standard Arabic (SA) language. Our fundamental
research goal is to select the most appropriate acoustic parameters
describing each audio frame, acoustic models and speech recognition
unit. To achieve this purpose, we analyze the effect of varying frame
windowing (size and period), acoustic parameter number resulting
from features extraction methods traditionally used in ASR, speech
recognition unit, Gaussian number per HMM state and number of
embedded re-estimations of the Baum-Welch Algorithm. To evaluate
the proposed ASR system, a multi-speaker SA connected-digits
corpus is collected, transcribed and used throughout all experiments.
A further evaluation is conducted on a speaker-independent continue
SA speech corpus. The phonemes recognition rate is 94.02% which is
relatively high when comparing it with another ASR system
evaluated on the same corpus.
Abstract: In this paper we present a new approach to detecting a
flaw in T.O.F.D (Time Of Flight Diffraction) type ultrasonic image
based on texture features. Texture is one of the most important
features used in recognizing patterns in an image. The paper
describes texture features based on 2D Gabor functions, i.e.,
Gaussian shaped band-pass filters, with dyadic treatment of the radial
spatial frequency range and multiple orientations, which represent an
appropriate choice for tasks requiring simultaneous measurement in
both space and frequency domains. The most relevant features are
used as input data on a Fuzzy c-mean clustering classifier. The
classes that exist are only two: 'defects' or 'no defects'. The proposed
approach is tested on the T.O.F.D image achieved at the laboratory
and on the industrial field.
Abstract: Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen's Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or character recognition, robot control and medical diagnosis, its potential as a robust substitute for clustering analysis remains relatively unresearched. SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization. In this paper, SOM is used for creating a toroidal mapping of two-dimensional lattice to perform cluster analysis on results of a chemical analysis of wines produced in the same region in Italy but derived from three different cultivators, referred to as the “wine recognition data" located in the University of California-Irvine database. The results are encouraging and it is believed that SOM would make an appealing and powerful decision-support system tool for clustering tasks and for data visualization.
Abstract: The design of a gravity dam is performed through an
interactive process involving a preliminary layout of the structure
followed by a stability and stress analysis. This study presents a
method to define the optimal top width of gravity dam with genetic
algorithm. To solve the optimization task (minimize the cost of the
dam), an optimization routine based on genetic algorithms (GAs) was
implemented into an Excel spreadsheet. It was found to perform well
and GA parameters were optimized in a parametric study. Using the
parameters found in the parametric study, the top width of gravity
dam optimization was performed and compared to a gradient-based
optimization method (classic method). The accuracy of the results
was within close proximity. In optimum dam cross section, the ratio
of is dam base to dam height is almost equal to 0.85, and ratio of dam
top width to dam height is almost equal to 0.13. The computerized
methodology may provide the help for computation of the optimal
top width for a wide range of height of a gravity dam.
Abstract: In the present work we investigate both the elastic and
electric properties of a chiral material. We consider a composite
structure made from a polymer matrix and anisotropic inclusions of
GaAs taking into account piezoelectric and dielectric properties of
the composite material. The principal task of the work is the
estimation of the functional properties of the composite material.
Abstract: Product Data Management (PDM) systems for Computer
Aided Design (CAD) file management are widely established
in design processes. This management system is indispensable for
design collaboration or when design task distribution is present. It is
thus surprising that engineering design curricula has not paid much
attention in the education of PDM systems. This is also the case
for eduction of ecodesign and environmental evaluation of products.
With the rise of sustainability as a strategic aspect in companies,
environmental concerns are becoming a key issue in design. This
paper discusses the establishment of a PDM platform to be used
among technical and vocational schools in Austria. The PDM system
facilitates design collaboration among these schools. Further, it will
be discussed how the PDM system has been prepared in order to
facilitate environmental evaluation of parts, components and subassemblies
of a product. By integrating a Business Intelligence
solution, environmental Life Cycle Assessment and communication
of results is enabled.
Abstract: To explore pipelines is one of various bio-mimetic
robot applications. The robot may work in common buildings such as
between ceilings and ducts, in addition to complicated and massive
pipeline systems of large industrial plants. The bio-mimetic robot finds
any troubled area or malfunction and then reports its data. Importantly,
it can not only prepare for but also react to any abnormal routes in the
pipeline. The pipeline monitoring tasks require special types of mobile
robots. For an effective movement along a pipeline, the movement of
the robot will be similar to that of insects or crawling animals. During
its movement along the pipelines, a pipeline monitoring robot has an
important task of finding the shapes of the approaching path on the
pipes. In this paper we propose an effective solution to the pipeline
pattern recognition, based on the fuzzy classification rules for the
measured IR distance data.
Abstract: This study aims to investigate the gender differences in
spatial navigation using the tasks of 2-D matrix navigation and
recognition of real driving scene. The results can be summarized as
followings. First, female subjects responded faster in 2-D matrix
navigation task than male subjects when landmark instructions were
provided. Second, in recognition task, male subjects recognized the
key elements involved in the past driving scene more accurately than
female subjects. In particular, female subjects tended to miss
peripheral information. These results suggest the possibility of gender
differences in spatial navigation.
Abstract: Support vector machines (SVMs) are considered to be
the best machine learning algorithms for minimizing the predictive
probability of misclassification. However, their drawback is that for
large data sets the computation of the optimal decision boundary is a
time consuming function of the size of the training set. Hence several
methods have been proposed to speed up the SVM algorithm. Here
three methods used to speed up the computation of the SVM
classifiers are compared experimentally using a musical genre
classification problem. The simplest method pre-selects a random
sample of the data before the application of the SVM algorithm. Two
additional methods use proximity graphs to pre-select data that are
near the decision boundary. One uses k-Nearest Neighbor graphs and
the other Relative Neighborhood Graphs to accomplish the task.
Abstract: This paper deals with the extraction of information from the experts to automatically identify and recognize Ganoderma infection in oil palm stem using tomography images. Expert-s knowledge are used as rules in a Fuzzy Inference Systems to classify each individual patterns observed in he tomography image. The classification is done by defining membership functions which assigned a set of three possible hypotheses : Ganoderma infection (G), non Ganoderma infection (N) or intact stem tissue (I) to every abnormalities pattern found in the tomography image. A complete comparison between Mamdani and Sugeno style,triangular, trapezoids and mixed triangular-trapezoids membership functions and different methods of aggregation and defuzzification is also presented and analyzed to select suitable Fuzzy Inference System methods to perform the above mentioned task. The results showed that seven out of 30 initial possible combination of available Fuzzy Inference methods in MATLAB Fuzzy Toolbox were observed giving result close to the experts estimation.
Abstract: Steel surface defect detection is essentially one of
pattern recognition problems. Support Vector Machines (SVMs) are
known as one of the most proper classifiers in this application. In this
paper, we introduce a more accurate classification method by using
SVMs as our final classifier of the inspection system. In this scheme,
multiclass classification task is performed based on the "one-againstone"
method and different kernels are utilized for each pair of the
classes in multiclass classification of the different defects.
In the proposed system, a decision tree is employed in the first
stage for two-class classification of the steel surfaces to "defect" and
"non-defect", in order to decrease the time complexity. Based on
the experimental results, generated from over one thousand images,
the proposed multiclass classification scheme is more accurate than
the conventional methods and the overall system yields a sufficient
performance which can meet the requirements in steel manufacturing.
Abstract: The purposes of researches - to estimate implicit ethnic attitudes by direct and indirect methods, to determine the accordance of two types measuring, to investigate influence of task type used in an experiment, on the results of measuring, as well as to determine a presence or communication between recent episodic events and chronologic correlations of ethnic attitudes. Method of the implicit measuring - an evaluative priming (EPT) carried out with the use of different SOA intervals, explicit methods of research are G.Soldatova-s types of ethnic identity, G.Soldatova-s index of tolerance, E.Bogardus scale of social distance. During five stages of researches received results open some aspects of implicit measuring, its correlation with the results of self-reports on different SOA intervals, connection of implicit measuring with emotional valence of episodic events of participants and other indexes, presenting a contribution to the decision of implicit measuring application problem for study of different social constructs
Abstract: It is an important task in Korean-English machine
translation to classify the gender of names correctly. When a sentence
is composed of two or more clauses and only one subject is given as a proper noun, it is important to find the gender of the proper noun
for correct translation of the sentence. This is because a singular pronoun has a gender in English while it does not in Korean. Thus,
in Korean-English machine translation, the gender of a proper noun should be determined. More generally, this task can be expanded into the classification of the general Korean names. This paper proposes a statistical method for this problem. By considering a name as just
a sequence of syllables, it is possible to get a statistics for each name from a collection of names. An evaluation of the proposed method
yields the improvement in accuracy over the simple looking-up of the
collection. While the accuracy of the looking-up method is 64.11%, that of the proposed method is 81.49%. This implies that the proposed
method is more plausible for the gender classification of the Korean names.