Abstract: We report the size dependence of 1D superconductivity in ultrathin (10-130 nm) nanowires produced by coating suspended carbon nanotubes with a superconducting NbN thin film. The resistance-temperature characteristic curves for samples with ≧25 nm wire width show the superconducting transition. On the other hand, for the samples with 10-nm width, the superconducting transition is not exhibited owing to the quantum size effect. The differential resistance vs. current density characteristic curves show some peak, indicating that Josephson junctions are formed in nanowires. The presence of the Josephson junctions is well explained by the measurement of the magnetic field dependence of the critical current. These understanding allow for the further expansion of the potential application of NbN, which is utilized for single photon detectors and so on.
Abstract: This research attempts to study the feasibility of
augmenting an augmented reality (AR) image card on a Quick
Response (QR) code. The authors have developed a new visual tag,
which contains a QR code and an augmented AR image card. The new
visual tag has features of reading both of the revealed data of the QR
code and the instant data from the AR image card. Furthermore, a
handheld communicating device is used to read and decode the new
visual tag, and then the concealed data of the new visual tag can be
revealed and read through its visual display. In general, the QR code is
designed to store the corresponding data or, as a key, to access the
corresponding data from the server through internet. Those reveled
data from the QR code are represented in text. Normally, the AR
image card is designed to store the corresponding data in
3-Dimensional or animation/video forms. By using QR code's
property of high fault tolerant rate, the new visual tag can access those
two different types of data by using a handheld communicating device.
The new visual tag has an advantage of carrying much more data than
independent QR code or AR image card. The major findings of this
research are: 1) the most efficient area for the designed augmented AR
card augmenting on the QR code is 9% coverage area out of the total
new visual tag-s area, and 2) the best location for the augmented AR
image card augmenting on the QR code is located in the bottom-right
corner of the new visual tag.
Abstract: Prior research has not effectively investigated how the
profitability of Chinese branches affect FDIs in China [1, 2], so this
study for the first time incorporates realistic earnings information
to systematically investigate effects of innovation, imitation, and
profit factors of FDI diffusions from Taiwan to China. Our nonlinear
least square (NLS) model, which incorporates earnings factors,
forms a nonlinear ordinary differential equation (ODE) in numerical
simulation programs. The model parameters are obtained through
a genetic algorithms (GA) technique and then optimized with the
collected data for the best accuracy. Particularly, Taiwanese regulatory
FDI restrictions are also considered in our modified model to meet
the realistic conditions. To validate the model-s effectiveness, this
investigation compares the prediction accuracy of modified model
with the conventional diffusion model, which does not take account
of the profitability factors.
The results clearly demonstrate the internal influence to be positive,
as early FDI adopters- consistent praises of FDI attract potential firms
to make the same move. The former erects a behavior model for the
latter to imitate their foreign investment decision. Particularly, the
results of modified diffusion models show that the earnings from
Chinese branches are positively related to the internal influence. In
general, the imitating tendency of potential consumers is substantially
hindered by the losses in the Chinese branches, and these firms would
invest less into China. The FDI inflow extension depends on earnings
of Chinese branches, and companies will adjust their FDI strategies
based on the returns. Since this research has proved that earning is
an influential factor on FDI dynamics, our revised model explicitly
performs superior in prediction ability than conventional diffusion
model.
Abstract: In order to study floristic and molecular classification
of common wild wheat (Triticum boeoticum Boiss.), an analysis was
conducted on populations of the Triticum boeoticum collected from
different regions of Iran. Considering all floristic compositions of
habitats, six floristic groups (syntaxa) within the populations were
identified. A high level of variation of T. boeoticum also detected
using SSR markers. Our results showed that molecular method
confirmed the grouping of floristic method. In other word, the results
from our study indicate that floristic classification are still useful,
efficient, and economic tools for characterizing the amount and
distribution of genetic variation in natural populations of T.
boeoticum. Nevertheless, molecular markers appear as useful and
complementary techniques for identification and for evaluation of
genetic diversity in studied populations.
Abstract: The main objective of seismic rehabilitation in the
foundations is decreasing the range of horizontal and vertical
vibrations and omitting high frequencies contents under the seismic
loading. In this regard, the advantages of micropiles network is
utilized. Reduction in vibration range of foundation can be achieved
by using high dynamic rigidness module such as deep foundations. In
addition, natural frequency of pile and soil system increases in regard
to rising of system rigidness. Accordingly, the main strategy is
decreasing of horizontal and vertical seismic vibrations of the
structure. In this case, considering the impact of foundation, pile and
improved soil foundation is a primary concern. Therefore, in this
paper, effective factors are studied on the seismic rehabilitation of
foundations applying network micropiles in sandy soils with
nonlinear reaction.
Abstract: This paper proposes a new decision making structure
to determine the appropriate product delivery strategy for different products in a manufacturing system among make-to-stock, make-toorder,
and hybrid strategy. Given product delivery strategies for all products in the manufacturing system, the position of the Order
Penetrating Point (OPP) can be located regarding the delivery strategies among which location of OPP in hybrid strategy is a
cumbersome task. In this regard, we employ analytic network process, because there are varieties of interrelated driving factors
involved in choosing the right location. Moreover, the proposed structure is augmented with fuzzy sets theory in order to cope with
the uncertainty of judgments. Finally, applicability of the proposed structure is proven in practice through a real industrial case company.
The numerical results demonstrate the efficiency of the proposed decision making structure in order partitioning and OPP location.
Abstract: Sustainable energy usage has been recognized as one
of the important measure to increase the competitiveness of the
nation globally. Many strong emphases were given in the Ninth
Malaysia Plan (RMK9) to improve energy efficient especially to
government buildings. With this in view, a project to investigate the
potential of energy saving in selected building in Universiti Tun
Hussein Onn Malaysia (UTHM) was carried out. In this project, a
case study involving electric energy consumption of the academic
staff office building was conducted. The scope of the study include to
identify energy consumption in a selected building, to study energy
saving opportunities, to analyse cost investment in term of economic
and to identify users attitude with respect to energy usage. The
MS1525:2001, Malaysian Standard -Code of practice on energy
efficiency and use of renewable energy for non-residential buildings
was used as reference. Several energy efficient measures were
considered and their merits and priority were compared. Improving
human behavior can reduce energy consumption by 6% while
technical measure can reduce energy consumption by 44%. Two
economic analysis evaluation methods were applied; they are the
payback period method and net present value method.
Abstract: This paper present a circular patch microstrip array antenna operate in KU-band (10.9GHz – 17.25GHz). The proposed circular patch array antenna will be in light weight, flexible, slim and compact unit compare with current antenna used in KU-band. The paper also presents the detail steps of designing the circular patch microstrip array antenna. An Advance Design System (ADS) software is used to compute the gain, power, radiation pattern, and S11 of the antenna. The proposed Circular patch microstrip array antenna basically is a phased array consisting of 'n' elements (circular patch antennas) arranged in a rectangular grid. The size of each element is determined by the operating frequency. The incident wave from satellite arrives at the plane of the antenna with equal phase across the surface of the array. Each 'n' element receives a small amount of power in phase with the others. There are feed network connects each element to the microstrip lines with an equal length, thus the signals reaching the circular patches are all combined in phase and the voltages add up. The significant difference of the circular patch array antenna is not come in the phase across the surface but in the magnitude distribution.
Abstract: This article proposes a novel Pareto-based multiobjective
meta-heuristic algorithm named non-dominated ranking
genetic algorithm (NRGA) to solve multi-facility location-allocation
problem. In NRGA, a fitness value representing rank is assigned to
each individual of the population. Moreover, two features ranked
based roulette wheel selection including select the fronts and choose
solutions from the fronts, are utilized. The proposed solving
methodology is validated using several examples taken from the
specialized literature. The performance of our approach shows that
NRGA algorithm is able to generate true and well distributed Pareto
optimal solutions.
Abstract: In this paper we describe our efforts to design and
implement an agent development framework that has the potential to
scale to the size of any underlying network suitable for various ECommerce
activities. The main novelty in our framework is it-s
capability to allow the development of sophisticated, secured agents
which are simple enough to be practical.
We have adopted FIPA agent platform reference Model as
backbone for implementation along with XML for agent
Communication and Java Cryptographic Extension and architecture
to realize the security of communication information between agents.
The advantage of our architecture is its support of agents
development in different languages and Communicating with each
other using a more open standard i.e. XML
Abstract: Residual dye contents in textile dyeing wastewater have complex aromatic structures that are resistant to degrade in biological wastewater treatment. The objectives of this study were to determine the effectiveness of nanoscale zerovalent iron (NZVI) to decolorize Reactive Black 5 (RB5) and Reactive Red 198 (RR198) in synthesized wastewater and to investigate the effects of the iron particle size, iron dosage and solution pHs on the destruction of RB5 and RR198. Synthesized NZVI was confirmed by transmission electron microscopy (TEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The removal kinetic rates (kobs) of RB5 (0.0109 min-1) and RR198 (0.0111 min-1) by 0.5% NZVI were many times higher than those of microscale zerovalent iron (ZVI) (0.0007 min-1 and 0.0008 min-1, respectively). The iron dosage increment exponentially increased the removal efficiencies of both RB5 and RR198. Additionally, lowering pH from 9 to 5 increased the decolorization kinetic rates of both RB5 and RR198 by NZVI. The destruction of azo bond (N=N) in the chromophore of both reactive dyes led to decolorization of dye solutions.
Abstract: In this paper smooth trajectories are computed in the Lie group SO(2, 1) as a motion planning problem by assigning a Frenet frame to the rigid body system to optimize the cost function of the elastic energy which is spent to track a timelike curve in Minkowski space. A method is proposed to solve a motion planning problem that minimize the integral of the square norm of Darboux vector of a timelike curve. This method uses the coordinate free Maximum Principle of Optimal control and results in the theory of integrable Hamiltonian systems. The presence of several conversed quantities inherent in these Hamiltonian systems aids in the explicit computation of the rigid body motions.
Abstract: Power system stability enhancement by simultaneous tuning of a Power System Stabilizer (PSS) and a Static Var Compensator (SVC)-based controller is thoroughly investigated in this paper. The coordination among the proposed damping stabilizers and the SVC internal voltage regulators has also been taken into consideration. The design problem is formulated as an optimization problem with a time-domain simulation-based objective function and Real-Coded Genetic Algorithm (RCGA) is employed to search for optimal controller parameters. The proposed stabilizers are tested on a weakly connected power system with different disturbances and loading conditions. The nonlinear simulation results are presented to show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions and disturbances. Further, the proposed design approach is found to be robust and improves stability effectively even under small disturbance and unbalanced fault conditions.
Abstract: Artificial Neural Network (ANN)s are best suited for
prediction and optimization problems. Trained ANNs have found
wide spread acceptance in several antenna design systems. Four
parameters namely antenna radiation resistance, loss resistance, efficiency,
and inductance can be used to design an antenna layout though
there are several other parameters available. An ANN can be trained
to provide the best and worst case precisions of an antenna design
problem defined by these four parameters. This work describes the
use of an ANN to generate the four mentioned parameters for a loop
antenna for the specified frequency range. It also provides insights
to the prediction of best and worst-case design problems observed
in applications and thereby formulate a model for physical layout
design of a loop antenna.
Abstract: This paper presents a novel method for remaining
useful life prediction using the Elliptical Basis Function (EBF)
network and a Markov chain. The EBF structure is trained by a
modified Expectation-Maximization (EM) algorithm in order to take
into account the missing covariate set. No explicit extrapolation is
needed for internal covariates while a Markov chain is constructed to
represent the evolution of external covariates in the study. The
estimated external and the unknown internal covariates constitute an
incomplete covariate set which are then used and analyzed by the EBF
network to provide survival information of the asset. It is shown in the
case study that the method slightly underestimates the remaining
useful life of an asset which is a desirable result for early maintenance
decision and resource planning.
Abstract: Internet is without any doubt the fastest and effective mean of communication making it possible to reach a great number of people in the world. It draws its base from exchange points. Indeed exchange points are used to inter-connect various Internet suppliers and operators in order to allow them to exchange traffic and it is with these interconnections that Internet made its great strides. They thus make it possible to limit the traffic delivered via the operators of transits. This limitation allows a significant improvement of the quality of service, a reduction in the latency time just as a reduction of the cost of connection for the final subscriber. Through this article we will show how the installation of an IXP allows an improvement and a diversification of the services just as a reduction of the Internet connection costs.
Abstract: This paper presents the methodology from machine
learning approaches for short-term rain forecasting system. Decision
Tree, Artificial Neural Network (ANN), and Support Vector Machine
(SVM) were applied to develop classification and prediction models
for rainfall forecasts. The goals of this presentation are to
demonstrate (1) how feature selection can be used to identify the
relationships between rainfall occurrences and other weather
conditions and (2) what models can be developed and deployed for
predicting the accurate rainfall estimates to support the decisions to
launch the cloud seeding operations in the northeastern part of
Thailand. Datasets collected during 2004-2006 from the
Chalermprakiat Royal Rain Making Research Center at Hua Hin,
Prachuap Khiri khan, the Chalermprakiat Royal Rain Making
Research Center at Pimai, Nakhon Ratchasima and Thai
Meteorological Department (TMD). A total of 179 records with 57
features was merged and matched by unique date. There are three
main parts in this work. Firstly, a decision tree induction algorithm
(C4.5) was used to classify the rain status into either rain or no-rain.
The overall accuracy of classification tree achieves 94.41% with the
five-fold cross validation. The C4.5 algorithm was also used to
classify the rain amount into three classes as no-rain (0-0.1 mm.),
few-rain (0.1- 10 mm.), and moderate-rain (>10 mm.) and the overall
accuracy of classification tree achieves 62.57%. Secondly, an ANN
was applied to predict the rainfall amount and the root mean square
error (RMSE) were used to measure the training and testing errors of
the ANN. It is found that the ANN yields a lower RMSE at 0.171 for
daily rainfall estimates, when compared to next-day and next-2-day
estimation. Thirdly, the ANN and SVM techniques were also used to
classify the rain amount into three classes as no-rain, few-rain, and
moderate-rain as above. The results achieved in 68.15% and 69.10%
of overall accuracy of same-day prediction for the ANN and SVM
models, respectively. The obtained results illustrated the comparison
of the predictive power of different methods for rainfall estimation.
Abstract: In this paper, determining the optimal proportionalintegral-
derivative (PID) controller gains of an single-area load
frequency control (LFC) system using genetic algorithm (GA) is
presented. The LFC is notoriously difficult to control optimally using
conventionally tuning a PID controller because the system parameters
are constantly changing. It is for this reason the GA as tuning strategy
was applied. The simulation has been conducted in MATLAB
Simulink package for single area power system. the simulation results
shows the effectiveness performance of under various disturbance.
Abstract: Although lots of research work has been done for
human pose recognition, the view-point of cameras is still critical
problem of overall recognition system. In this paper, view-point
insensitive human pose recognition is proposed. The aims of the
proposed system are view-point insensitivity and real-time processing.
Recognition system consists of feature extraction module, neural
network and real-time feed forward calculation. First, histogram-based
method is used to extract feature from silhouette image and it is
suitable for represent the shape of human pose. To reduce the
dimension of feature vector, Principle Component Analysis(PCA) is
used. Second, real-time processing is implemented by using Compute
Unified Device Architecture(CUDA) and this architecture improves
the speed of feed-forward calculation of neural network. We
demonstrate the effectiveness of our approach with experiments on
real environment.
Abstract: Tool wear and surface roughness prediction plays a
significant role in machining industry for proper planning and control
of machining parameters and optimization of cutting conditions. This
paper deals with developing an artificial neural network (ANN)
model as a function of cutting parameters in turning steel under
minimum quantity lubrication (MQL). A feed-forward
backpropagation network with twenty five hidden neurons has been
selected as the optimum network. The co-efficient of determination
(R2) between model predictions and experimental values are 0.9915,
0.9906, 0.9761 and 0.9627 in terms of VB, VM, VS and Ra
respectively. The results imply that the model can be used easily to
forecast tool wear and surface roughness in response to cutting
parameters.