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: Applied a mouse-s roller with a gripper to increase the
efficiency for a gripper can learn to a material handling without
slipping. To apply a gripper, we use the optimize principle to develop
material handling by use a signal for checking a roller mouse that
rotate or not. In case of the roller rotates means that the material slips.
A gripper will slide to material handling until the roller will not
rotate. As this experiment has test material handling for comparing a
grip force that uses to material handling of the 10-human with the
applied gripper. We can summarize that human exert the material
handling more than the applied gripper. Because of the gripper can
exert more befit to material handling than human and may be a
minimum force to lift a material without slipping.
Abstract: The eco-efficient use of “waste" makes sense from
economic, social, and environmental perspectives. By efficiency diverting “waste" products back into useful and/or profitable inputs,
industries and entire societies can reap the benefits of improved financial profit, decreased environmental degradation, and overall
well-being of humanity.
In this project, several material flows at
Company Limited were investigated. Principles of "industrial ecology" were applied to improve the management of waste rubbers that are used in the jewelry manufacturing process. complete this project, a brief engineering analysis stream, and investigated eco-efficient principles for more efficient
handling of the materials and wastes were conducted, and the result were used to propose implementation strategies.
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: Let n ≥ 3 be an integer and G2(n) be the subgroup
of square roots of 1 in (Z/nZ)*. In this paper, we give an algorithm
that computes a generating set of this subgroup.
Abstract: WebGL is typically used with web browsers. In this
paper, we represent a standalone WebGL execution environment,
where the original WebGL source codes show the same result to those
of WebGL-capable web browsers. This standalone environment
enables us to run WebGL programs without web browsers and/or
internet connections. Our implementation shows the same rendering
results with typical web browser outputs. This standalone environment
is suitable for low-tier devices and/or debugging purposes.
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: Current research on semantic web aims at making intelligent web pages meaningful for machines. In this way, ontology plays a primary role. We believe that logic can help ontology languages (such as OWL) to be more fluent and efficient. In this paper we try to combine logic with OWL to reduce some disadvantages of this language. Therefore we extend OWL by logic and also show how logic can satisfy our future expectations of an ontology language.
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: The objective of this research was to find the diffusion properties of vehicles on the road by using the V-Sphere Code. The diffusion coefficient and the size of the height of the wake were estimated with the LES option and the third order MUSCL scheme. We evaluated the code with the changes in the moments of Reynolds Stress along the mean streamline. The results show that at the leading part of a bluff body the LES has some advantages over the RNS since the changes in the strain rates are larger for the leading part. We estimated that the diffusion coefficient with the computed Reynolds stress (non-dimensional) was about 0.96 times the mean velocity.
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: The introduction of sowing technologies into minimum- or no-tillage soil has a number of economical and environmental virtues, such as improving soil properties, decreasing soil erosion and degradation, and saving working time and fuel. However, the main disadvantage of these technologies is that plant residues on the soil surface reduce the quality of the planted crop seeds, thus requiring plant residues to be removed or cut. This paper presents a analysis of disc coulter parameters and an experimental investigation of cutting spring barley straw containing various amounts of moisture with different disc coulters (smooth and notched).
Abstract: The impact of noise upon live quality has become an
important aspect to make both urban and environmental policythroughout
Europe and in Turkey. Concern over the quality of urban
environments, including noise levels and declining quality of green
space, is over the past decade with increasing emphasis on designing
livable and sustainable communities. According to the World Health
Organization, noise pollution is the third most hazardous
environmental type of pollution which proceeded by only air (gas
emission) and water pollution. The research carried out in two
phases, the first stage of the research noise and plant types providing
the suction of noise was evaluated through literature study and at the
second stage, definite types (Juniperus horizontalis L., Spirea
vanhouetti Briot., Cotoneaster dammerii C.K., Berberis thunbergii
D.C., Pyracantha coccinea M. etc.) were selected for the city of
Konya. Trials were conducted on the highway of Konya. The biggest
value of noise reduction was 6.3 dB(A), 4.9 dB(A), 6.2 dB(A) value
with compared to the control which includes the group that formed
by the bushes at the distance of 7m, 11m, 20m from the source and
5m, 9m, 20m of plant width, respectively. In this paper, definitions
regarding to noise and its sources were made and the precautions
were taken against to noise that mentioned earlier with the adverse
effects of noise. Plantation design approaches and suggestions
concerning to the diversity to be used, which are peculiar to roadside,
were developed to discuss the role and the function of plant material
to reduce the noise of the traffic.
Abstract: With increasing complexity in electronic systems
there is a need for system level anomaly detection and fault isolation.
Anomaly detection based on vector similarity to a training set is used
in this paper through two approaches, one the preserves the original
information, Mahalanobis Distance (MD), and the other that
compresses the data into its principal components, Projection Pursuit
Analysis. These methods have been used to detect deviations in
system performance from normal operation and for critical parameter
isolation in multivariate environments. The study evaluates the
detection capability of each approach on a set of test data with known
faults against a baseline set of data representative of such “healthy"
systems.
Abstract: In the literature of information theory, there is
necessity for comparing the different measures of fuzzy entropy and
this consequently, gives rise to the need for normalizing measures of
fuzzy entropy. In this paper, we have discussed this need and hence
developed some normalized measures of fuzzy entropy. It is also
desirable to maximize entropy and to minimize directed divergence
or distance. Keeping in mind this idea, we have explained the method
of optimizing different measures of fuzzy entropy.
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%.