Abstract: The exhaustive quality control is becoming more and
more important when commercializing competitive products in the
world's globalized market. Taken this affirmation as an undeniable
truth, it becomes critical in certain sector markets that need to offer
the highest restrictions in quality terms. One of these examples is the
percussion cap mass production, a critical element assembled in
firearm ammunition. These elements, built in great quantities at a
very high speed, must achieve a minimum tolerance deviation in
their fabrication, due to their vital importance in firing the piece of
ammunition where they are built in. This paper outlines a machine
vision development for the 100% inspection of percussion caps
obtaining data from 2D and 3D simultaneous images. The acquisition
speed and precision of these images from a metallic reflective piece
as a percussion cap, the accuracy of the measures taken from these
images and the multiple fabrication errors detected make the main
findings of this work.
Abstract: This paper presents a highly efficient algorithm for detecting and tracking humans and objects in video surveillance sequences. Mean shift clustering is applied on backgrounddifferenced image sequences. For efficiency, all calculations are performed on integral images. Novel corresponding exponential integral kernels are introduced to allow the application of nonuniform kernels for clustering, which dramatically increases robustness without giving up the efficiency of the integral data structures. Experimental results demonstrating the power of this approach are presented.
Abstract: The urban centers within northeastern Brazil are
mainly influenced by the intense rainfalls, which can occur after long
periods of drought, when flood events can be observed during such
events. Thus, this paper aims to study the rainfall frequencies in such
region through the wavelet transform. An application of wavelet
analysis is done with long time series of the total monthly rainfall
amount at the capital cities of northeastern Brazil. The main
frequency components in the time series are studied by the global
wavelet spectrum and the modulation in separated periodicity bands
were done in order to extract additional information, e.g., the 8 and
16 months band was examined by an average of all scales, giving a
measure of the average annual variance versus time, where the
periods with low or high variance could be identified. The important
increases were identified in the average variance for some periods,
e.g. 1947 to 1952 at Teresina city, which can be considered as high
wet periods. Although, the precipitation in those sites showed similar
global wavelet spectra, the wavelet spectra revealed particular
features. This study can be considered an important tool for time
series analysis, which can help the studies concerning flood control,
mainly when they are applied together with rainfall-runoff
simulations.
Abstract: Classifying data hierarchically is an efficient approach
to analyze data. Data is usually classified into multiple categories, or
annotated with a set of labels. To analyze multi-labeled data, such
data must be specified by giving a set of labels as a semantic range.
There are some certain purposes to analyze data. This paper shows
which multi-labeled data should be the target to be analyzed for
those purposes, and discusses the role of a label against a set of
labels by investigating the change when a label is added to the set of
labels. These discussions give the methods for the advanced analysis
of multi-labeled data, which are based on the role of a label against
a semantic range.
Abstract: Minimization methods for training feed-forward networks with Backpropagation are compared. Feedforward network training is a special case of functional minimization, where no explicit model of the data is assumed. Therefore due to the high dimensionality of the data, linearization of the training problem through use of orthogonal basis functions is not desirable. The focus is functional minimization on any basis. A number of methods based on local gradient and Hessian matrices are discussed. Modifications of many methods of first and second order training methods are considered. Using share rates data, experimentally it is proved that Conjugate gradient and Quasi Newton?s methods outperformed the Gradient Descent methods. In case of the Levenberg-Marquardt algorithm is of special interest in financial forecasting.
Abstract: The analysis is mainly concentrating on the knowledge
management literatures productivity trend which subjects as
“knowledge management" in SSCI database. The purpose what the
analysis will propose is to summarize the trend information for
knowledge management researchers since core knowledge will be
concentrated in core categories. The result indicated that the literature
productivity which topic as “knowledge management" is still
increasing extremely and will demonstrate the trend by different
categories including author, country/territory, institution name,
document type, language, publication year, and subject area. Focus on
the right categories, you will catch the core research information. This
implies that the phenomenon "success breeds success" is more
common in higher quality publications.
Abstract: Although many researchers have studied the flow
hydraulics in compound channels, there are still many complicated problems in determination of their flow rating curves. Many different
methods have been presented for these channels but extending them
for all types of compound channels with different geometrical and
hydraulic conditions is certainly difficult. In this study, by aid of nearly 400 laboratory and field data sets of geometry and flow rating
curves from 30 different straight compound sections and using artificial neural networks (ANNs), flow discharge in compound channels was estimated. 13 dimensionless input variables including relative depth, relative roughness, relative width, aspect ratio, bed
slope, main channel side slopes, flood plains side slopes and berm
inclination and one output variable (flow discharge), have been used
in ANNs. Comparison of ANNs model and traditional method
(divided channel method-DCM) shows high accuracy of ANNs model results. The results of Sensitivity analysis showed that the relative depth with 47.6 percent contribution, is the most effective input parameter for flow discharge prediction. Relative width and
relative roughness have 19.3 and 12.2 percent of importance, respectively. On the other hand, shape parameter, main channel and
flood plains side slopes with 2.1, 3.8 and 3.8 percent of contribution, have the least importance.
Abstract: This paper attempts to explain response components of Electrovestibulography (EVestG) using a computer simulation of a three-canal model of the vestibular system. EVestG is a potentially new diagnostic method for Meniere's disease. EVestG is a variant of Electrocochleography (ECOG), which has been used as a standard method for diagnosing Meniere's disease - it can be used to measure the SP/AP ratio, where an SP/AP ratio greater than 0.4-0.5 is indicative of Meniere-s Disease. In EVestG, an applied head tilt replaces the acoustic stimulus of ECOG. The EVestG output is also an SP/AP type plot, where SP is the summing potential, and AP is the action potential amplitude. AP is thought of as being proportional to the size of a population of afferents in an excitatory neural firing state. A simulation of the fluid volume displacement in the vestibular labyrinth in response to various types of head tilts (ipsilateral, backwards and horizontal rotation) was performed, and a simple neural model based on these simulations developed. The simple neural model shows that the change in firing rate of the utricle is much larger in magnitude than the change in firing rates of all three semi-circular canals following a head tilt (except in a horizontal rotation). The data suggests that the change in utricular firing rate is a minimum 2-3 orders of magnitude larger than changes in firing rates of the canals during ipsilateral/backward tilts. Based on these results, the neural response recorded by the electrode in our EVestG recordings is expected to be dominated by the utricle in ipsilateral/backward tilts (It is important to note that the effect of the saccule and efferent signals were not taken into account in this model). If the utricle response dominates the EVestG recordings as the modeling results suggest, then EVestG has the potential to diagnose utricular hair cell damage due to a viral infection (which has been cited as one possible cause of Meniere's Disease).
Abstract: The objectives of this research were to compare the success of SME registered in Nakorn Pathom Province divided in personal data also to study the relations between the innovation knowledge and capability and the success of SME registered in Nakorn Pathom Province and to study the relations between the work efficiency and the success of SME registered in Nakorn Pathom Province. A questionnaire was utilized as a tool to collect data. Statistics utilized in this research included frequency, percentage, mean, standard deviation, and multiple regression analysis. Data were analyzed by using Statistical Package for the Social Sciences.The findings revealed that the majority of respondents were male with the age between 25-34 years old, hold undergraduate degree, married and stay together. The average income of respondents was between 10,001-20,000 baht. It also found that in terms of innovation knowledge and capability, there were two variables had an influence on the amount of innovation knowledge and capability, innovation evaluation which were physical characteristic and innovation process.
Abstract: In this paper, a wavelet based method is proposed to
identify the constant coefficients of a second order linear system and
is compared with the least squares method. The proposed method
shows improved accuracy of parameter estimation as compared to the
least squares method. Additionally, it has the advantage of smaller
data requirement and storage requirement as compared to the least
squares method.
Abstract: Geographical Information Systems are an integral part
of planning in modern technical systems. Nowadays referred to as
Spatial Decision Support Systems, as they allow synergy database
management systems and models within a single user interface
machine and they are important tools in spatial design for
evaluating policies and programs at all levels of administration.
This work refers to the creation of a Geographical Information
System in the context of a broader research in the area of influence
of an under construction station of the new metro in the Greek
city of Thessaloniki, which included statistical and multivariate
data analysis and diagrammatic representation, mapping and
interpretation of the results.
Abstract: The purpose of this study was to investigate the
relationship between parent involvement and preschool disabled
children’s development. Parents of 3 year old disabled children
(N=440) and 5 year old disabled children (N=937) participating in the
Special Needs Education Longitudinal Study were interviewed or
answered the web design questionnaire about their actions in parenting
their disabled children. These children’s developments were also
evaluated by their teachers. Data were analyzed using Structural
Equation Modeling. Results were showed by tables and figures. Based
on the results, the researcher made some suggestions for future studies.
Abstract: Rutting is one of the major load-related distresses in airport flexible pavements. Rutting in paving materials develop gradually with an increasing number of load applications, usually appearing as longitudinal depressions in the wheel paths and it may be accompanied by small upheavals to the sides. Significant research has been conducted to determine the factors which affect rutting and how they can be controlled. Using the experimental design concepts, a series of tests can be conducted while varying levels of different parameters, which could be the cause for rutting in airport flexible pavements. If proper experimental design is done, the results obtained from these tests can give a better insight into the causes of rutting and the presence of interactions and synergisms among the system variables which have influence on rutting. Although traditionally, laboratory experiments are conducted in a controlled fashion to understand the statistical interaction of variables in such situations, this study is an attempt to identify the critical system variables influencing airport flexible pavement rut depth from a statistical DoE perspective using real field data from a full-scale test facility. The test results do strongly indicate that the response (rut depth) has too much noise in it and it would not allow determination of a good model. From a statistical DoE perspective, two major changes proposed for this experiment are: (1) actual replication of the tests is definitely required, (2) nuisance variables need to be identified and blocked properly. Further investigation is necessary to determine possible sources of noise in the experiment.
Abstract: The experiments were performed in a batch set up
under different concentrations of Cu (II) (0.2 g.l-1 to 0.9 g.l-1), pH (4-
6), temperatures (20oC – 40oC) with varying teak leaves powder (as
biosorbent) dosage of 0.3 g.l-1 to 0.5 g.l-1. The kinetics of interactions
were tested with pseudo first order Lagergran equation and the value
for k1 was found to be 6.909 x 10-3 min-1. The biosorption data gave
a good fit with Langmuir and Fruendlich isotherms and the Langmuir
monolayer capacity (qm) was found to be 166.78 mg. g-1. Similarly
the Freundlich adsorption capacity (Kf) was estimated as 2.49 l g-1.
The mean values of the thermodynamic parameters ΔH, ΔS, and ΔG
were -62.42 KJ. mol-1, -0.219 KJ.mol-1 K-1 and -1.747 KJ.mol-1 at
293 K from a solution containing 0.4 g l-1 of Cu(II) showing the
biosorption to be thermodynamically favourable. These results show
good potentiality of using teak leaves as a biosorbent for the removal
of Cu(II) from aqueous solutions.
Abstract: Aeration by a plunging water jet is an energetically attractive way to effect oxygen-transfer than conventional oxygenation systems. In the present study, a new type of conical shaped plunging aeration device is fabricated to generate hollow inclined ined plunging jets (jet plunge angle of π/3 ) to investigate its oxygen transfer capacity. The results suggest that the volumetric oxygen-transfer coefficient and oxygen-transfer efficiency of the conical plunging jet aerator are competitive with other types of aeration systems. Relationships of volumetric oxygen-transfer coefficient with jet power per unit volume and jet parameters are also proposed. The suggested relationships predict the volumetric oxygentransfer coefficient within a scatter of ± 15% . Further, the application of Support Vector Machines on the experimental data revealed its utility in the prediction of volumetric oxygen-transfer coefficient and development of conical plunging jet aerators.
Abstract: Demand over web services is in growing with increases number of Web users. Web service is applied by Web application. Web application size is affected by its user-s requirements and interests. Differential in requirements and interests lead to growing of Web application size. The efficient way to save store spaces for more data and information is achieved by implementing algorithms to compress the contents of Web application documents. This paper introduces an algorithm to reduce Web application size based on reduction of the contents of HTML files. It removes unimportant contents regardless of the HTML file size. The removing is not ignored any character that is predicted in the HTML building process.
Abstract: MicroRNAs are an important class of gene expression
regulators that are involved in many biological processes including
embryogenesis. miR-125b is a conserved microRNA that is enriched
in the nervous system. We have previously reported the function of
miR-125b in neuronal differentiation of human cell lines. We also
discovered the function of miR-125b in regulating p53 in human and
zebrafish. Here we further characterize the brain defects in zebrafish
embryos injected with morpholinos against miR-125b. Our data
confirm the essential role of miR-125b in brain morphogenesis
particularly in maintaining the balance between proliferation, cell
death and differentiation. We identified lunatic fringe (lfng) as an
additional target of miR-125b in human and zebrafish and suggest
that lfng may mediate the function of miR-125b in neurogenesis.
Together, this report reveals new insights into the function of miR-
125b during neural development of zebrafish.
Abstract: Synthetic aperture radar (SAR) imaging usually
requires echo data collected continuously pulse by pulse with certain
bandwidth. However in real situation, data collection or part of signal
spectrum can be interrupted due to various reasons, i.e. there will be
gaps in spatial spectrum. In this case we need to find ways to fill out
the resulted gaps and get image with defined resolution. In this paper
we introduce our work on how to apply iterative spatially variant
apodization (Super-SVA) technique to extrapolate the spatial
spectrum in both azimuthal and range directions so as to fill out the
gaps and get correct radar image.
Abstract: Multiple-Input-Multiple-Output (MIMO) is one of the most important communication techniques that allow wireless systems to achieve higher data rate. To overcome the practical difficulties in implementing Dirty Paper Coding (DPC), various suboptimal MIMO Broadcast (MIMO-BC) scheduling algorithms are employed which choose the best set of users among all the users. In this paper we discuss such a sub-optimal MIMO-BC scheduling algorithm which employs antenna selection at the receiver side. The channels for the users considered here are not Identical and Independent Distributed (IID) so that users at the receiver side do not get equal opportunity for communication. So we introduce a method of applying weights to channels of the users which are not IID in such a way that each of the users gets equal opportunity for communication. The effect of weights on overall sum-rate achieved by the system has been investigated and presented.
Abstract: Recent scientific investigations indicate that
multimodal biometrics overcome the technical limitations of
unimodal biometrics, making them ideally suited for everyday life
applications that require a reliable authentication system. However,
for a successful adoption of multimodal biometrics, such systems
would require large heterogeneous datasets with complex multimodal
fusion and privacy schemes spanning various distributed
environments. From experimental investigations of current
multimodal systems, this paper reports the various issues related to
speed, error-recovery and privacy that impede the diffusion of such
systems in real-life. This calls for a robust mechanism that caters to
the desired real-time performance, robust fusion schemes,
interoperability and adaptable privacy policies.
The main objective of this paper is to present a framework that
addresses the abovementioned issues by leveraging on the
heterogeneous resource sharing capacities of Grid services and the
efficient machine learning capabilities of artificial neural networks
(ANN). Hence, this paper proposes a Grid-based neural network
framework for adopting multimodal biometrics with the view of
overcoming the barriers of performance, privacy and risk issues that
are associated with shared heterogeneous multimodal data centres.
The framework combines the concept of Grid services for reliable
brokering and privacy policy management of shared biometric
resources along with a momentum back propagation ANN (MBPANN)
model of machine learning for efficient multimodal fusion and
authentication schemes. Real-life applications would be able to adopt
the proposed framework to cater to the varying business requirements
and user privacies for a successful diffusion of multimodal
biometrics in various day-to-day transactions.