Abstract: Support vector regression (SVR) has been regarded
as a state-of-the-art method for approximation and regression. The
importance of kernel function, which is so-called admissible support
vector kernel (SV kernel) in SVR, has motivated many studies
on its composition. The Gaussian kernel (RBF) is regarded as a
“best" choice of SV kernel used by non-expert in SVR, whereas
there is no evidence, except for its superior performance on some
practical applications, to prove the statement. Its well-known that
reproducing kernel (R.K) is also a SV kernel which possesses many
important properties, e.g. positive definiteness, reproducing property
and composing complex R.K by simpler ones. However, there are a
limited number of R.Ks with explicit forms and consequently few
quantitative comparison studies in practice. In this paper, two R.Ks,
i.e. SV kernels, composed by the sum and product of a translation
invariant kernel in a Sobolev space are proposed. An exploratory
study on the performance of SVR based general R.K is presented
through a systematic comparison to that of RBF using multiple
criteria and synthetic problems. The results show that the R.K is
an equivalent or even better SV kernel than RBF for the problems
with more input variables (more than 5, especially more than 10) and
higher nonlinearity.
Abstract: This paper focuses on developing an integrated
reliable and sophisticated model for ultra large wind turbines And to
study the performance and analysis of vector control on large wind
turbines. With the advance of power electronics technology, direct
driven multi-pole radial flux PMSG (Permanent Magnet Synchronous
Generator) has proven to be a good choice for wind turbines
manufacturers. To study the wind energy conversion systems, it is
important to develop a wind turbine simulator that is able to produce
realistic and validated conditions that occur in real ultra MW wind
turbines. Three different packages are used to simulate this model,
namely, Turbsim, FAST and Simulink. Turbsim is a Full field wind
simulator developed by National Renewable Energy Laboratory
(NREL). The wind turbine mechanical parts are modeled by FAST
(Fatigue, Aerodynamics, Structures and Turbulence) code which is
also developed by NREL. Simulink is used to model the PMSG, full
scale back to back IGBT converters, and the grid.
Abstract: The photocatalytic activity efficiency of TiO2 for the degradation of Toluene in photoreactor can be enhanced by nano- TiO2/LDPE composite film. Since the amount of TiO2 affected the efficiency of the photocatalytic activity, this work was mainly concentrated on the effort to embed the high amount of TiO2 in the Polyethylene matrix. The developed photocatalyst was characterized by XRD, UV-Vis spectrophotometer and SEM. The SEM images revealed the high homogeneity of the deposition of TiO2 on the polyethylene matrix. The XRD patterns interpreted that TiO2 embedded in the PE matrix exhibited mainly in anatase form. In addition, the photocatalytic results show that the toluene removal efficiencies of 30±5%, 49±4%, 68±5%, 42±6% and 33±5% were obtained when using the catalyst loading at 0%, 10%, 15%, 25% and 50% (wt. cat./wt. film), respectively.
Abstract: Describes the current situation of educational Robotics
"the State of the art" its concept, its evolution their niches of
opportunity, academic and business and the importance of education
and academic outreach. It shows that the development of high-tech
automated educational materials influence the teaching-learning
process and that communication between machines and humans is a
reality.
Abstract: In this paper, the gain spectrum of EDFA has been broadened by implementing HTE configuration for S and C band. On using this configuration an amplification bandwidth of 76nm ranging from 1479nm to 1555nm with a peak gain of 26dB has been obtained.
Abstract: Although face recognition seems as an easy task for
human, automatic face recognition is a much more challenging task
due to variations in time, illumination and pose. In this paper, the
influence of time-lapse on visible and thermal images is examined.
Orthogonal moment invariants are used as a feature extractor to
analyze the effect of time-lapse on thermal and visible images and the
results are compared with conventional Principal Component
Analysis (PCA). A new triangle square ratio criterion is employed
instead of Euclidean distance to enhance the performance of nearest
neighbor classifier. The results of this study indicate that the ideal
feature vectors can be represented with high discrimination power
due to the global characteristic of orthogonal moment invariants.
Moreover, the effect of time-lapse has been decreasing and enhancing
the accuracy of face recognition considerably in comparison with
PCA. Furthermore, our experimental results based on moment
invariant and triangle square ratio criterion show that the proposed
approach achieves on average 13.6% higher in recognition rate than
PCA.
Abstract: As in today's semiconductor industries test costs can make up to 50 percent of the total production costs, an efficient test error detection becomes more and more important. In this paper, we present a new machine learning approach to test error detection that should provide a faster recognition of test system faults as well as an improved test error recall. The key idea is to learn a classifier ensemble, detecting typical test error patterns in wafer test results immediately after finishing these tests. Since test error detection has not yet been discussed in the machine learning community, we define central problem-relevant terms and provide an analysis of important domain properties. Finally, we present comparative studies reflecting the failure detection performance of three individual classifiers and three ensemble methods based upon them. As base classifiers we chose a decision tree learner, a support vector machine and a Bayesian network, while the compared ensemble methods were simple and weighted majority vote as well as stacking. For the evaluation, we used cross validation and a specially designed practical simulation. By implementing our approach in a semiconductor test department for the observation of two products, we proofed its practical applicability.
Abstract: Electro Chemical Discharge Machining (ECDM) is an
emerging hybrid machining process used in precision machining of hard and brittle non-conducting materials. The present paper gives a
critical review on materials machined by ECDM under the prevailing machining conditions; capability indicators of the process are
reported. Some results obtained while performing experiments in micro-channeling on soda lime glass using ECDM are also presented. In these experiments, Tool Wear (TW) and Material Removal (MR)
were studied using design of experiments and L–4 orthogonal array. Experimental results showed that the applied voltage was the most influencing parameter in both MR and TW studies. Field
emission scanning electron microscopy (FESEM) results obtained on the microchannels confirmed the presence of micro-cracks, primarily responsible for MR. Chemical etching was also seen along the edges.
The Energy dispersive spectroscopy (EDS) results were used to detect the elements present in the debris and specimens.
Abstract: Dengue fever is prevalent in Malaysia with numerous
cases including mortality recorded over the years. Public education
on the prevention of the desease through various means has been
carried out besides the enforcement of legal means to eradicate
Aedes mosquitoes, the dengue vector breeding ground. Hence, other
means need to be explored, such as predicting the seasonal peak
period of the dengue outbreak and identifying related climate factors
contributing to the increase in the number of mosquitoes. Simulation
model can be employed for this purpose. In this study, we created a
simulation of system dynamic to predict the spread of dengue
outbreak in Hulu Langat, Selangor Malaysia. The prototype was
developed using STELLA 9.1.2 software. The main data input are
rainfall, temperature and denggue cases. Data analysis from the graph
showed that denggue cases can be predicted accurately using these
two main variables- rainfall and temperature. However, the model
will be further tested over a longer time period to ensure its
accuracy, reliability and efficiency as a prediction tool for dengue
outbreak.
Abstract: In this study, the Multi-Layer Perceptron (MLP)with Back-Propagation learning algorithm are used to classify to effective diagnosis Parkinsons disease(PD).It-s a challenging problem for medical community.Typically characterized by tremor, PD occurs due to the loss of dopamine in the brains thalamic region that results in involuntary or oscillatory movement in the body. A feature selection algorithm along with biomedical test values to diagnose Parkinson disease.Clinical diagnosis is done mostly by doctor-s expertise and experience.But still cases are reported of wrong diagnosis and treatment. Patients are asked to take number of tests for diagnosis.In many cases,not all the tests contribute towards effective diagnosis of a disease.Our work is to classify the presence of Parkinson disease with reduced number of attributes.Original,22 attributes are involved in classify.We use Information Gain to determine the attributes which reduced the number of attributes which is need to be taken from patients.The Artificial neural networks is used to classify the diagnosis of patients.Twenty-Two attributes are reduced to sixteen attributes.The accuracy is in training data set is 82.051% and in the validation data set is 83.333%.
Abstract: Innovation is being view from four areas of
innovation, product, service, technology, and marketing. Whereas
customer loyalty is composed of customer expectation, perceived
quality, perceived value, corporate image, customer satisfaction,
customer trust/confidence, customer commitment, customer
complaint, and customer loyalty. This study aimed to investigate the
influence of innovation factors to customer loyalty to GSM in the
telecom companies where use of products and services. Structural
Equation Modeling (SEM) using to analyze innovation factors. It was
found the factor of innovation have significant influence on customer
loyalty.
Abstract: Majority of researches conducted on Iranian urban
development plans indicate that they have been almost unsuccessful
in terms of draft, execution and goal achievement. Lack or shortage
of essential statistics and information can be listed as an important
reason of the failure of these plans. Lack of figures and information
has turned into an obvious part of the country-s statistics officials.
This problem has made urban planner themselves to embark on
physical surveys including real estate and land pricing, population
and economic census of the city. Apart from the problems facing
urban developers, the possibility of errors is high in such surveys.
In the present article, applying the interview technique, it has
been mentioned that utilizing multipurpose cadastre system as a land
information system is essential for urban development plans in Iran.
It can minimize or even remove the failures facing urban
development plans.
Abstract: The flow field over a three dimensional pole barn
characterized by a cylindrical roof has been numerically investigated.
Wind pressure and viscous loads acting on the agricultural building
have been analyzed for several incoming wind directions, so as to
evaluate the most critical load condition on the structure. A constant
wind velocity profile, based on the maximum reference wind speed in
the building site (peak gust speed worked out for 50 years return
period) and on the local roughness coefficient, has been simulated.
In order to contemplate also the hazard due to potential air
wedging between the stored hay and the lower part of the ceiling, the
effect of a partial filling of the barn has been investigated.
The distribution of wind-induced loads on the structure have been
determined, allowing a numerical quantification of the effect of wind
direction on the induced stresses acting on a hemicylindrical roof.
Abstract: This study presents a mathematical modeling approach to the planning of HIV therapies on an individual basis. The model replicates clinical data from typical-progressors to AIDS for all stages of the disease with good agreement. Clinical data from rapid-progressors and long-term non-progressors is also matched by estimation of immune system parameters only. The ability of the model to reproduce these phenomena validates the formulation, a fact which is exploited in the investigation of effective therapies. The therapy investigation suggests that, unlike continuous therapy, structured treatment interruptions (STIs) are able to control the increase in both the drug-sensitive and drug-resistant virus population and, hence, prevent the ultimate progression from HIV to AIDS. The optimization results further suggest that even patients characterised by the same progression type can respond very differently to the same treatment and that the latter should be designed on a case-by-case basis. Such a methodology is presented here.
Abstract: Testing is an activity that is required both in the
development and maintenance of the software development life cycle
in which Integration Testing is an important activity. Integration
testing is based on the specification and functionality of the software
and thus could be called black-box testing technique. The purpose of
integration testing is testing integration between software
components. In function or system testing, the concern is with overall
behavior and whether the software meets its functional specifications
or performance characteristics or how well the software and
hardware work together. This explains the importance and necessity
of IT for which the emphasis is on interactions between modules and
their interfaces. Software errors should be discovered early during
IT to reduce the costs of correction. This paper introduces a new type
of integration error, presenting an overview of Integration Testing
techniques with comparison of each technique and also identifying
which technique detects what type of error.
Abstract: In this study, an investigation over digestive diseases has been done in which the sound acts as a detector medium. Pursue to the preprocessing the extracted signal in cepstrum domain is registered. After classification of digestive diseases, the system selects random samples based on their features and generates the interest nonstationary, long-term signals via inverse transform in cepstral domain which is presented in digital and sonic form as the output. This structure is updatable or on the other word, by receiving a new signal the corresponding disease classification is updated in the feature domain.
Abstract: In this paper, we investigate the appearance of the giant component in random subgraphs G(p) of a given large finite graph family Gn = (Vn, En) in which each edge is present independently with probability p. We show that if the graph Gn satisfies a weak isoperimetric inequality and has bounded degree, then the probability p under which G(p) has a giant component of linear order with some constant probability is bounded away from zero and one. In addition, we prove the probability of abnormally large order of the giant component decays exponentially. When a contact graph is modeled as Gn, our result is of special interest in the study of the spread of infectious diseases or the identification of community in various social networks.
Abstract: Much time series data is generally from continuous dynamic system. Firstly, this paper studies the detection of the nonlinearity of time series from continuous dynamics systems by applying the Phase-randomized surrogate algorithm. Then, the Delay Vector Variance (DVV) method is introduced into nonlinearity test. The results show that under the different sampling conditions, the opposite detection of nonlinearity is obtained via using traditional test statistics methods, which include the third-order autocovariance and the asymmetry due to time reversal. Whereas the DVV method can perform well on determining nonlinear of Lorenz signal. It indicates that the proposed method can describe the continuous dynamics signal effectively.
Abstract: Rapid advancement in computing technology brings
computers and humans to be seamlessly integrated in future. The
emergence of smartphone has driven computing era towards
ubiquitous and pervasive computing. Recognizing human activity has
garnered a lot of interest and has raised significant researches-
concerns in identifying contextual information useful to human
activity recognition. Not only unobtrusive to users in daily life,
smartphone has embedded built-in sensors that capable to sense
contextual information of its users supported with wide range
capability of network connections. In this paper, we will discuss the
classification algorithms used in smartphone-based human activity.
Existing technologies pertaining to smartphone-based researches in
human activity recognition will be highlighted and discussed. Our
paper will also present our findings and opinions to formulate
improvement ideas in current researches- trends. Understanding
research trends will enable researchers to have clearer research
direction and common vision on latest smartphone-based human
activity recognition area.
Abstract: Information and Communication Technologies (ICT) in mathematical education is a very active field of research and innovation, where learning is understood to be meaningful and grasping multiple linked representation rather than rote memorization, a great amount of literature offering a wide range of theories, learning approaches, methodologies and interpretations, are generally stressing the potentialities for teaching and learning using ICT. Despite the utilization of new learning approaches with ICT, students experience difficulties in learning concepts relevant to understanding mathematics, much remains unclear about the relationship between the computer environment, the activities it might support, and the knowledge that might emerge from such activities. Many questions that might arise in this regard: to what extent does the use of ICT help students in the process of understanding and solving tasks or problems? Is it possible to identify what aspects or features of students' mathematical learning can be enhanced by the use of technology? This paper will highlight the interest of the integration of information and communication technologies (ICT) into the teaching and learning of mathematics (quadratic functions), it aims to investigate the effect of four instructional methods on students- mathematical understanding and problem solving. Quantitative and qualitative methods are used to report about 43 students in middle school. Results showed that mathematical thinking and problem solving evolves as students engage with ICT activities and learn cooperatively.