Abstract: Sharing consistent and correct master data among
disparate applications in a reverse-logistics chain has long been
recognized as an intricate problem. Although a master data
management (MDM) system can surely assume that responsibility,
applications that need to co-operate with it must comply with
proprietary query interfaces provided by the specific MDM system. In
this paper, we present a RFID-ready MDM system which makes
master data readily available for any participating applications in a
reverse-logistics chain. We propose a RFID-wrapper as a part of our
MDM. It acts as a gateway between any data retrieval request and
query interfaces that process it. With the RFID-wrapper, any
participating applications in a reverse-logistics chain can easily
retrieve master data in a way that is analogous to retrieval of any other
RFID-based logistics transactional data.
Abstract: In this study, Li4SiO4 powder was successfully
synthesized via sol gel method followed by drying at 150oC. Lithium
oxide, Li2O and silicon oxide, SiO2 were used as the starting
materials with citric acid as the chelating agent. The obtained powder
was then sintered at various temperatures. Crystallographic phase
analysis, morphology and ionic conductivity were investigated
systematically employing X-ray diffraction, Fourier Transform
Infrared, Scanning Electron Microscopy and AC impedance
spectroscopy. XRD result showed the formation of pure monoclinic
Li4SiO4 crystal structure with lattice parameters a = 5.140 Å, b =
6.094 Å, c = 5.293 Å, β = 90o in the sample sintered at 750oC. This
observation was confirmed by FTIR analysis. The bulk conductivity
of this sample at room temperature was 3.35 × 10-6 S cm-1 and the
highest bulk conductivity of 1.16 × 10-4 S cm-1 was obtained at
100°C. The results indicated that, the Li4SiO4 compound has
potential to be used as host for LISICON structured solid electrolyte
for low temperature application.
Abstract: In this paper developed and realized absolutely new
algorithm for solving three-dimensional Poisson equation. This
equation used in research of turbulent mixing, computational fluid
dynamics, atmospheric front, and ocean flows and so on. Moreover in
the view of rising productivity of difficult calculation there was
applied the most up-to-date and the most effective parallel
programming technology - MPI in combination with OpenMP
direction, that allows to realize problems with very large data
content. Resulted products can be used in solving of important
applications and fundamental problems in mathematics and physics.
Abstract: The possibility of radionuclides-related contamination
of lands at agricultural holdings defines the necessity to apply special
protective measures in plant growing. The aim of researches is to
elucidate the influence of polymers applying on biological migration
of man-made anthropogenic radionuclides 90Sr and 137Cs in the
system water - soil – plant. The tests are being carried out under field
conditions with and without application of polymers in root-inhabited
media in more radioecological tension zone (with the radius of 7 km
from the Armenian Nuclear Power Plant). The polymers on the base
of K+, Caµ, KµCaµ ions were tested. Productivity of pepper
depending on the presence and type of polymer material, content of
artificial radionuclides in waters, soil and plant material has been
determined. The character of different polymers influence on the
artificial radionuclides migration and accumulation in the system
water-soil-plant and accumulation in the plants has been cleared up.
Abstract: Optical 3D measurement of objects is meaningful in
numerous industrial applications. In various cases shape acquisition
of weak textured objects is essential. Examples are repetition parts
made of plastic or ceramic such as housing parts or ceramic bottles as
well as agricultural products like tubers. These parts are often
conveyed in a wobbling way during the automated optical inspection.
Thus, conventional 3D shape acquisition methods like laser scanning
might fail. In this paper, a novel approach for acquiring 3D shape of
weak textured and moving objects is presented. To facilitate such
measurements an active stereo vision system with structured light is
proposed. The system consists of multiple camera pairs and auxiliary
laser pattern generators. It performs the shape acquisition within one
shot and is beneficial for rapid inspection tasks. An experimental
setup including hardware and software has been developed and
implemented.
Abstract: The elimimation of mefenamic acid has been carried
out by photolysis, ozonation, adsorption onto activated carbon (AC)
and combinations of the previous single systems (O3+AC and
O3+UV). The results obtained indicate that mefenamic acid is not
photo-reactive, showing a relatively low quantum yield of the order
of 6 x 10-4 mol Einstein-1. Application of ozone to mefenamic
aqueous solutions instantaneously eliminates the pharmaceutical,
achieving simultaneously a 40% of mineralization. Addition of AC to
the ozonation process does not enhance the process, moreover,
mineralization is completely inhibited if compared to results obtained
by single ozonation. The combination of ozone and UV radiation led
to the best results in terms of mineralization (60% after 120 min).
Abstract: The successful implementation of Service-Oriented Architecture (SOA) is not confined to Information Technology systems and required changes of the whole enterprise. In order to adapt IT and business, the enterprise requires adequate and measurable methods. The adoption of SOA creates new problem with regard to measuring and analysis the performance. In fact the enterprise should investigate to what extent the development of services will increase the value of business. It is required for every business to measure the extent of SOA adaptation with the goals of enterprise. Moreover, precise performance metrics and their combination with the advanced evaluation methodologies as a solution should be defined. The aim of this paper is to present a systematic methodology for designing a measurement system at the technical and business levels, so that: (1) it will determine measurement metrics precisely (2) the results will be analysed by mapping identified metrics to the measurement tools.
Abstract: Japan is known to be a technological powerhouse, being noted for its automobiles, consumer electronics, laptop computers, portable gaming devices, and more recently healing animal robots. Japan is also noted for its popular culture; manga, anime, novels, films, character goods, game programs, cosplay cafes, karaoke and so on. It may be natural for people outside Japan to assume that e-learning in Japan must be well advanced and innovative. In reality, the application of technologies in education in Japan is far behind of other developed countries. Especially in higher education, apathy of students towards their study prevails and teachers continue ignoring such student attitudes. E-learning, which is supposed to revolutionalize the way people learn as it has potentials to enable more student-centered learning, has not been realized in Japan and mostly used to perpetuate the teachercentered teaching in a different format.
Abstract: Shape memory alloy (SMA) actuators have found a
wide range of applications due to their unique properties such as high
force, small size, lightweight and silent operation. This paper presents
the development of compact (SMA) actuator and cooling system in
one unit. This actuator is developed for multi-fingered hand. It
consists of nickel-titanium (Nitinol) SMA wires in compact forming.
The new arrangement insulates SMA wires from the human body by
housing it in a heat sink and uses a thermoelectric device for rejecting
heat to improve the actuator performance. The study uses
optimization methods for selecting the SMA wires geometrical
parameters and the material of a heat sink. The experimental work
implements the actuator prototype and measures its response.
Abstract: Spare parts inventory management is one of the major
areas of inventory research. Analysis of recent literature showed that
an approach integrating spare parts classification, demand
forecasting, and stock control policies is essential; however, adapting
this integrated approach is limited. This work presents an integrated
framework for spare part inventory management and an Excel based
application developed for the implementation of the proposed
framework. A multi-criteria analysis has been used for spare
classification. Forecasting of spare parts- intermittent demand has
been incorporated into the application using three different
forecasting models; namely, normal distribution, exponential
smoothing, and Croston method. The application is also capable of
running with different inventory control policies. To illustrate the
performance of the proposed framework and the developed
application; the framework is applied to different items at a service
organization. The results achieved are presented and possible areas
for future work are highlighted.
Abstract: The ability of the brain to organize information and generate the functional structures we use to act, think and communicate, is a common and easily observable natural phenomenon. In object-oriented analysis, these structures are represented by objects. Objects have been extensively studied and documented, but the process that creates them is not understood. In this work, a new class of discrete, deterministic, dissipative, host-guest dynamical systems is introduced. The new systems have extraordinary self-organizing properties. They can host information representing other physical systems and generate the same functional structures as the brain does. A simple mathematical model is proposed. The new systems are easy to simulate by computer, and measurements needed to confirm the assumptions are abundant and readily available. Experimental results presented here confirm the findings. Applications are many, but among the most immediate are object-oriented engineering, image and voice recognition, search engines, and Neuroscience.
Abstract: As a simple to method estimate the plant heating energy
capacity of an apartment complex, a new load calculation method has
been proposed. The method which can be called as unit building
method, predicts the heating load of the entire complex instead of
summing up that of each apartment belonging to complex.
Comparison of the unit heating load for various floor sizes between the
present method and conventional approach shows a close agreement
with dynamic load calculation code. Some additional calculations are
performed to demonstrate it-s application examples.
Abstract: The study was performed to evaluate the effect of Sgirdling,
fruit thinning plus bagging with 2,4-D application, fruit
thinning plus bagging on growth and quality of wax apple fruit.
Girdling was applied three week before flowering. The 2,4-D was
sprayed at the small bud and petal fall stage. The effect of all
treatments on fruit growth was measured weekly. The physical and
biochemical quality characteristics of the fruits were recorded. The
results showed that no significant effect on number of bud among
treatments. S-girdling, 2,4-D application produced the lowest bud
drop, fruit drop compared to untreated control. Moreover, S-girdling
enhanced faster fruit growth producing the best final fruit length and
diameter than the control treatment. It was also observed that Sgirdling
greatly increased fruit set, fruit weight as well as total
soluble solid, reduced fruit crack, and titratable acidity. In
conclusion, S-girdling had a distinctive and significant effect on most
of the fruit quality characteristics assessed. Application 2,4-D was
also recommended as the industry norm to increase fruit set, and fruit
quality in wax apple.
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: 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: A concern that researchers usually face in different
applications of Artificial Neural Network (ANN) is determination of
the size of effective domain in time series. In this paper, trial and
error method was used on groundwater depth time series to determine
the size of effective domain in the series in an observation well in
Union County, New Jersey, U.S. different domains of 20, 40, 60, 80,
100, and 120 preceding day were examined and the 80 days was
considered as effective length of the domain. Data sets in different
domains were fed to a Feed Forward Back Propagation ANN with
one hidden layer and the groundwater depths were forecasted. Root
Mean Square Error (RMSE) and the correlation factor (R2) of
estimated and observed groundwater depths for all domains were
determined. In general, groundwater depth forecast improved, as
evidenced by lower RMSEs and higher R2s, when the domain length
increased from 20 to 120. However, 80 days was selected as the
effective domain because the improvement was less than 1% beyond
that. Forecasted ground water depths utilizing measured daily data
(set #1) and data averaged over the effective domain (set #2) were
compared. It was postulated that more accurate nature of measured
daily data was the reason for a better forecast with lower RMSE
(0.1027 m compared to 0.255 m) in set #1. However, the size of input
data in this set was 80 times the size of input data in set #2; a factor
that may increase the computational effort unpredictably. It was
concluded that 80 daily data may be successfully utilized to lower the
size of input data sets considerably, while maintaining the effective
information in the data set.
Abstract: Residue Number System (RNS) is a modular representation and is proved to be an instrumental tool in many digital signal processing (DSP) applications which require high-speed computations. RNS is an integer and non weighted number system; it can support parallel, carry-free, high-speed and low power arithmetic. A very interesting correspondence exists between the concepts of Multiple Valued Logic (MVL) and Residue Number Arithmetic. If the number of levels used to represent MVL signals is chosen to be consistent with the moduli which create the finite rings in the RNS, MVL becomes a very natural representation for the RNS. There are two concerns related to the application of this Number System: reaching the most possible speed and the largest dynamic range. There is a conflict when one wants to resolve both these problem. That is augmenting the dynamic range results in reducing the speed in the same time. For achieving the most performance a method is considere named “One-Hot Residue Number System" in this implementation the propagation is only equal to one transistor delay. The problem with this method is the huge increase in the number of transistors they are increased in order m2 . In real application this is practically impossible. In this paper combining the Multiple Valued Logic and One-Hot Residue Number System we represent a new method to resolve both of these two problems. In this paper we represent a novel design of an OHRNS-based adder circuit. This circuit is useable for Multiple Valued Logic moduli, in comparison to other RNS design; this circuit has considerably improved the number of transistors and power consumption.
Abstract: This paper presents a NDT by infrared thermography with excitation CO2 Laser, wavelength of 10.6 μm. This excitation is the controllable heating beam, confirmed by a preliminary test on a wooden plate 1.2 m x 0.9 m x 1 cm. As the first practice, this method is applied to detecting the defect in CFRP heated by the Laser 300 W during 40 s. Two samples 40 cm x 40 cm x 4.5 cm are prepared, one with defect, another one without defect. The laser beam passes through the lens of a deviation device, and heats the samples placed at a determinate position and area. As a result, the absence of adhesive can be detected. This method displays prominently its application as NDT with the composite materials. This work gives a good perspective to characterize the laser beam, which is very useful for the next detection campaigns.
Abstract: Although backpropagation ANNs generally predict
better than decision trees do for pattern classification problems, they
are often regarded as black boxes, i.e., their predictions cannot be
explained as those of decision trees. In many applications, it is
desirable to extract knowledge from trained ANNs for the users to
gain a better understanding of how the networks solve the problems.
A new rule extraction algorithm, called rule extraction from artificial
neural networks (REANN) is proposed and implemented to extract
symbolic rules from ANNs. A standard three-layer feedforward ANN
is the basis of the algorithm. A four-phase training algorithm is
proposed for backpropagation learning. Explicitness of the extracted
rules is supported by comparing them to the symbolic rules generated
by other methods. Extracted rules are comparable with other methods
in terms of number of rules, average number of conditions for a rule,
and predictive accuracy. Extensive experimental studies on several
benchmarks classification problems, such as breast cancer, iris,
diabetes, and season classification problems, demonstrate the
effectiveness of the proposed approach with good generalization
ability.
Abstract: The feature extraction method(s) used to recognize
hand-printed characters play an important role in ICR applications.
In order to achieve high recognition rate for a recognition system, the
choice of a feature that suits for the given script is certainly an
important task. Even if a new feature required to be designed for a
given script, it is essential to know the recognition ability of the
existing features for that script. Devanagari script is being used in
various Indian languages besides Hindi the mother tongue of majority
of Indians. This research examines a variety of feature extraction
approaches, which have been used in various ICR/OCR applications,
in context to Devanagari hand-printed script. The study is conducted
theoretically and experimentally on more that 10 feature extraction
methods. The various feature extraction methods have been evaluated
on Devanagari hand-printed database comprising more than 25000
characters belonging to 43 alphabets. The recognition ability of the
features have been evaluated using three classifiers i.e. k-NN, MLP
and SVM.