Abstract: The aim of this study was to analyse the most
important parameters determining the quality of the motion structure
of the basic classical dance jump – grand jeté.Research sample
consisted of 8 students of the Dance Conservatory in Brno. Using the
system Simi motion we performed a 3D kinematic analysis of the
jump. On the basis of the comparison of structure quality and
measured data of the grand jeté, we defined the optimal values of the
relevant parameters determining the quality of the performance. The
take-off speed should achieve about 2.4 m·s-1, the optimum take-off
angle is 28 - 30º. The take-off leg should swing backward at the
beginning of the flight phase with the minimum speed of 3.3 m·s-1.If
motor abilities of dancers achieve the level necessary for optimal
performance of a classical dance jump, there is room for certain
variability of the structure of the dance jump.
Abstract: The paper describes ergonomics problems trend of
student at B5101 classroom building 2, Suranaree University of
Technology. The objective to survey ergonomics problems and effect
from use chairs for sitting in class room. The result from survey
method 100 student they use lecture chair for sitting in classroom
more than 2 hours/ day by RULA[1]. and Body discomfort survey[2].
The result from Body discomfort survey contribute fatigue problems
at neck, lower back, upper back and right shoulder 2.93, 2.91, 2.33,
1.75 respectively and result from RULA contribute fatigue problems
at neck, body and right upper arm 4.00, 3.75 and 3.00 respectively
are consistent. After that the researcher provide improvement plan
for design new chair support student fatigue reduction by prepare
data of sample anthropometry and design ergonomics chair prototype
3 unit. Then sample 100 student trial to use new chair and evaluate
again by RULA, Body discomfort and satisfaction. The result from
trial new chair after improvement by RULA present fatigue reduction
average of head and neck from 4.00 to 2.25 , body and trunk from
3.75 to 2.00 and arm force from 1.00 to 0.25 respectively. The result
from trial new chair after improvement by Body discomfort present
fatigue reduction average of lower back from 2.91 to 0.87, neck from
2.93 to 1.24, upper back 2.33 to 0.84 and right upper arm from 1.75
to 0.74. That statistical of RULA and Body discomfort survey
present fatigue reduction after improvement significance with a
confidence level of 95% (p-value 0.05). When analyzing the
relationship of fatigue as part of the body by Chi – square test during
RULA and Body discomfort that before and after improvements were
consistent with the significant level of confidence 95% (p-value 0.05)
. Moreover the students satisfaction result from trial with a new chair
for 30 minutes [3]. 72 percent very satisfied of the folding of the
secondary writing simple 66% the width of the writing plate, 64% the
suitability of the writing plate, 62% of soft seat cushion and 61%
easy to seat the chair.
Abstract: Fingerprint based identification system; one of a well
known biometric system in the area of pattern recognition and has
always been under study through its important role in forensic
science that could help government criminal justice community. In
this paper, we proposed an identification framework of individuals by
means of fingerprint. Different from the most conventional
fingerprint identification frameworks the extracted Geometrical
element features (GEFs) will go through a Discretization process.
The intention of Discretization in this study is to attain individual
unique features that could reflect the individual varianceness in order
to discriminate one person from another. Previously, Discretization
has been shown a particularly efficient identification on English
handwriting with accuracy of 99.9% and on discrimination of twins-
handwriting with accuracy of 98%. Due to its high discriminative
power, this method is adopted into this framework as an independent
based method to seek for the accuracy of fingerprint identification.
Finally the experimental result shows that the accuracy rate of
identification of the proposed system using Discretization is 100%
for FVC2000, 93% for FVC2002 and 89.7% for FVC2004 which is
much better than the conventional or the existing fingerprint
identification system (72% for FVC2000, 26% for FVC2002 and
32.8% for FVC2004). The result indicates that Discretization
approach manages to boost up the classification effectively, and
therefore prove to be suitable for other biometric features besides
handwriting and fingerprint.
Abstract: In the end of the day, meteorological data and environmental data becomes widely used such as plant varieties selection system. Variety plant selection for planted area is of almost importance for all crops, including varieties of sugarcane. Since sugarcane have many varieties. Variety plant non selection for planting may not be adapted to the climate or soil conditions for planted area. Poor growth, bloom drop, poor fruit, and low price are to be from varieties which were not recommended for those planted area. This paper presents plant varieties selection system for planted areas in Thailand from meteorological data and environmental data by the use of decision tree techniques. With this software developed as an environmental data analysis tool, it can analyze resulting easier and faster. Our software is a front end of WEKA that provides fundamental data mining functions such as classify, clustering, and analysis functions. It also supports pre-processing, analysis, and decision tree output with exporting result. After that, our software can export and display data result to Google maps API in order to display result and plot plant icons effectively.
Abstract: A new Feed-Forward/Feedback Generalized
Minimum Variance Pole-placement Controller to incorporate the
robustness of classical pole-placement into the flexibility of
generalized minimum variance self-tuning controller for Single-Input
Single-Output (SISO) has been proposed in this paper. The design,
which provides the user with an adaptive mechanism, which ensures
that the closed loop poles are, located at their pre-specified positions.
In addition, the controller design which has a feed-forward/feedback
structure overcomes the certain limitations existing in similar poleplacement
control designs whilst retaining the simplicity of
adaptation mechanisms used in other designs. It tracks set-point
changes with the desired speed of response, penalizes excessive
control action, and can be applied to non-minimum phase systems.
Besides, at steady state, the controller has the ability to regulate the
constant load disturbance to zero. Example simulation results using
both simulated and real plant models demonstrate the effectiveness of
the proposed controller.
Abstract: This study aims to determine the level of resistance of Hevea brasiliensis and Paraserianthes falcataria (synonym: Falcataria molucana) against wood rot fungi Pleurotus ostreatus based on Indonesian standard SNI 01.7207-2006 and Japanese standard JIS K 1571-2004. The variables measured were visual appearance and weight loss percentage of wood based on longitudinal and cross section fiber directions of rubber wood and sengon wood. Measurement of oven dry weight loss of wood samples performed after 12 weeks incubation. Replication performed was 10 times at each treatment combination. The results based on SNI 01.7207-2006, weight loss value of H. brasiliensis and P. falcataria wood with fiber direction longitudinal were 23,12 and 22,25% respectively and cross section were 20,77 and 18,76% respectively, and all were classified to resistance class IV (no resistance). The results based on JIS K 1571-2004, weight loss value of both woods with fiber direction cross section were 10,95 and 14,20% respectively.
Abstract: In this paper we are interested in classification problems
with a performance constraint on error probability. In such
problems if the constraint cannot be satisfied, then a rejection option
is introduced. For binary labelled classification, a number of SVM
based methods with rejection option have been proposed over the
past few years. All of these methods use two thresholds on the SVM
output. However, in previous works, we have shown on synthetic data
that using thresholds on the output of the optimal SVM may lead to
poor results for classification tasks with performance constraint. In
this paper a new method for supervised classification with rejection
option is proposed. It consists in two different classifiers jointly
optimized to minimize the rejection probability subject to a given
constraint on error rate. This method uses a new kernel based linear
learning machine that we have recently presented. This learning
machine is characterized by its simplicity and high training speed
which makes the simultaneous optimization of the two classifiers
computationally reasonable. The proposed classification method with
rejection option is compared to a SVM based rejection method
proposed in recent literature. Experiments show the superiority of
the proposed method.
Abstract: This paper proposes a novel hybrid algorithm for feature selection based on a binary ant colony and SVM. The final subset selection is attained through the elimination of the features that produce noise or, are strictly correlated with other already selected features. Our algorithm can improve classification accuracy with a small and appropriate feature subset. Proposed algorithm is easily implemented and because of use of a simple filter in that, its computational complexity is very low. The performance of the proposed algorithm is evaluated through a real Rotary Cement kiln dataset. The results show that our algorithm outperforms existing algorithms.
Abstract: Fault-proneness of a software module is the
probability that the module contains faults. A correlation exists
between the fault-proneness of the software and the measurable
attributes of the code (i.e. the static metrics) and of the testing (i.e.
the dynamic metrics). Early detection of fault-prone software
components enables verification experts to concentrate their time and
resources on the problem areas of the software system under
development. This paper introduces Genetic Algorithm based
software fault prediction models with Object-Oriented metrics. The
contribution of this paper is that it has used Metric values of JEdit
open source software for generation of the rules for the classification
of software modules in the categories of Faulty and non faulty
modules and thereafter empirically validation is performed. The
results shows that Genetic algorithm approach can be used for
finding the fault proneness in object oriented software components.
Abstract: In this paper, we demonstrate the adaptive
least-mean-square (LMS) filter modeling using SystemC. SystemC is
a modeling language that allows designer to model both hardware and
software component and makes it possible to design from high level
system of abstraction to low level system of abstraction. We produced
five adaptive least-mean-square filter models that are classed as five
abstraction levels using SystemC proceeding from the abstract model
to the more concrete model.
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: This paper investigates the problem of tracking spa¬tiotemporal changes of a satellite image through the use of Knowledge Discovery in Database (KDD). The purpose of this study is to help a given user effectively discover interesting knowledge and then build prediction and decision models. Unfortunately, the KDD process for spatiotemporal data is always marked by several types of imperfections. In our paper, we take these imperfections into consideration in order to provide more accurate decisions. To achieve this objective, different KDD methods are used to discover knowledge in satellite image databases. Each method presents a different point of view of spatiotemporal evolution of a query model (which represents an extracted object from a satellite image). In order to combine these methods, we use the evidence fusion theory which considerably improves the spatiotemporal knowledge discovery process and increases our belief in the spatiotemporal model change. Experimental results of satellite images representing the region of Auckland in New Zealand depict the improvement in the overall change detection as compared to using classical methods.
Abstract: Artificial Immune System (AIS) is relatively naive paradigm for intelligent computations. The inspiration for AIS is derived from natural Immune System (IS). Classically it is believed that IS strives to discriminate between self and non-self. Most of the existing AIS research is based on this approach. Danger Theory (DT) argues this approach and proposes that IS fights against danger producing elements and tolerates others. We, the computational researchers, are not concerned with the arguments among immunologists but try to extract from it novel abstractions for intelligent computation. This paper aims to follow DT inspiration for intelligent data processing. The approach may introduce new avenue in intelligent processing. The data used is system calls data that is potentially significant in intrusion detection applications.
Abstract: An application framework provides a reusable design
and implementation for a family of software systems. Application
developers extend the framework to build their particular
applications using hooks. Hooks are the places identified to show
how to use and customize the framework. Hooks define Framework
Interface Classes (FICs) and their possible specifications, which
helps in building reusable test cases for the implementations of these
classes. In applications developed using gray-box frameworks, FICs
inherit framework classes or use them without inheritance. In this
paper, a test-case generation technique is extended to build test cases
for FICs built for gray-box frameworks. A tool is developed to
automate the introduced technique.
Abstract: This paper addresses the problem of asymptotic tracking
control of a linear parabolic partial differential equation with indomain
point actuation. As the considered model is a non-standard
partial differential equation, we firstly developed a map that allows
transforming this problem into a standard boundary control problem
to which existing infinite-dimensional system control methods can
be applied. Then, a combination of energy multiplier and differential
flatness methods is used to design an asymptotic tracking controller.
This control scheme consists of stabilizing state-feedback derived
from the energy multiplier method and feed-forward control based
on the flatness property of the system. This approach represents
a systematic procedure to design tracking control laws for a class
of partial differential equations with in-domain point actuation. The
applicability and system performance are assessed by simulation
studies.
Abstract: Purpose: To explore the use of Curvelet transform to
extract texture features of pulmonary nodules in CT image and support
vector machine to establish prediction model of small solitary
pulmonary nodules in order to promote the ratio of detection and
diagnosis of early-stage lung cancer. Methods: 2461 benign or
malignant small solitary pulmonary nodules in CT image from 129
patients were collected. Fourteen Curvelet transform textural features
were as parameters to establish support vector machine prediction
model. Results: Compared with other methods, using 252 texture
features as parameters to establish prediction model is more proper.
And the classification consistency, sensitivity and specificity for the
model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based
on texture features extracted from Curvelet transform, support vector
machine prediction model is sensitive to lung cancer, which can
promote the rate of diagnosis for early-stage lung cancer to some
extent.
Abstract: A novel method of learning complex fuzzy decision regions in the n-dimensional feature space is proposed. Through the fuzzy decision regions, a given pattern's class membership value of every class is determined instead of the conventional crisp class the pattern belongs to. The n-dimensional fuzzy decision region is approximated by union of hyperellipsoids. By explicitly parameterizing these hyperellipsoids, the decision regions are determined by estimating the parameters of each hyperellipsoid.Genetic Algorithm is applied to estimate the parameters of each region component. With the global optimization ability of GA, the learned decision region can be arbitrarily complex.
Abstract: Within the last years, several technologies have been developed to help building e-learning portals. Most of them follow approaches that deliver a vast amount of functionalities, suitable for class-like learning. The SuGI project, as part of the D-Grid (funded by the BMBF), targets on delivering a highly scalable and sustainable learning solution to provide materials (e.g. learning modules, training systems, webcasts, tutorials, etc.) containing knowledge about Grid computing to the D-Grid community. In this article, the process of the development of an e-learning portal focused on the requirements of this special user group is described. Furthermore, it deals with the conceptual and technical design of an e-learning portal, addressing the special needs of heterogeneous target groups. The main focus lies on the quality management of the software development process, Web templates for uploading new contents, the rich search and filter functionalities which will be described from a conceptual as well as a technical point of view. Specifically, it points out best practices as well as concepts to provide a sustainable solution to a relatively unknown and highly heterogeneous community.
Abstract: This paper introduces a new instantaneous frequency
computation approach -Counting Instantaneous Frequency for a
general class of signals called simple waves. The classsimple wave
contains a wide range of continuous signals for which the concept
instantaneous frequency has a perfect physical sense. The concept of
-Counting Instantaneous Frequency also applies to all the discrete data.
For all the simple wave signals and the discrete data, -Counting
instantaneous frequency can be computed directly without signal
decomposition process. The intrinsic mode functions obtained through
empirical mode decomposition belongs to simple wave. So
-Counting instantaneous frequency can be used together with
empirical mode decomposition.
Abstract: A new class of fuzzy closed sets, namely fuzzy weakly closed set in a fuzzy topological space is introduced and it is established that this class of fuzzy closed sets lies between fuzzy closed sets and fuzzy generalized closed sets. Alongwith the study of fundamental results of such closed sets, we define and characterize fuzzy weakly compact space and fuzzy weakly closed space.