Abstract: Data stream analysis is the process of computing
various summaries and derived values from large amounts of data
which are continuously generated at a rapid rate. The nature of a
stream does not allow a revisit on each data element. Furthermore,
data processing must be fast to produce timely analysis results. These
requirements impose constraints on the design of the algorithms to
balance correctness against timely responses. Several techniques
have been proposed over the past few years to address these
challenges. These techniques can be categorized as either dataoriented
or task-oriented. The data-oriented approach analyzes a
subset of data or a smaller transformed representation, whereas taskoriented
scheme solves the problem directly via approximation
techniques. We propose a hybrid approach to tackle the data stream
analysis problem. The data stream has been both statistically
transformed to a smaller size and computationally approximated its
characteristics. We adopt a Monte Carlo method in the approximation
step. The data reduction has been performed horizontally and
vertically through our EMR sampling method. The proposed method
is analyzed by a series of experiments. We apply our algorithm on
clustering and classification tasks to evaluate the utility of our
approach.
Abstract: A Rotary Disc Contactor with inner diameter of
9.1cm and maximum operating height of 40cm has been used to
investigate break up phenomenon. Water-Toluene, Water as
continuous phase and Toluene as dispersed phase, was selected as
chemical system in the experiments. The mentioned chemical system
has high interfacial tension so it was possible to form big drops
which permit accurate investigation on break up phenomenon as well
as the first and second critical rotor speeds.
In this study, Break up phenomenon has been studied as a function
of mother drop size, rotor speed and continuous phase height. Further
more; the effects of mother drop size and continuous phase height on
the first and second critical rotor speeds were investigated. Finally,
two modified correlations were proposed to estimate the first and
second critical speeds.
Abstract: The main mission of Ezilla is to provide a friendly
interface to access the virtual machine and quickly deploy the high
performance computing environment. Ezilla has been developed by
Pervasive Computing Team at National Center for High-performance
Computing (NCHC). Ezilla integrates the Cloud middleware,
virtualization technology, and Web-based Operating System (WebOS)
to form a virtual computer in distributed computing environment. In
order to upgrade the dataset and speedup, we proposed the sensor
observation system to deal with a huge amount of data in the
Cassandra database. The sensor observation system is based on the
Ezilla to store sensor raw data into distributed database. We adopt the
Ezilla Cloud service to create virtual machines and login into virtual
machine to deploy the sensor observation system. Integrating the
sensor observation system with Ezilla is to quickly deploy experiment
environment and access a huge amount of data with distributed
database that support the replication mechanism to protect the data
security.
Abstract: Random Oracle Model (ROM) is an effective method
for measuring the practical security of cryptograph. In this paper, we
try to use it into information hiding system (IHS). Because IHS has its
own properties, the ROM must be modified if it is used into IHS.
Firstly, we fully discuss why and how to modify each part of ROM
respectively. The main changes include: 1) Divide the attacks that IHS
may be suffered into two phases and divide the attacks of each phase
into several kinds. 2) Distinguish Oracles and Black-boxes clearly. 3)
Define Oracle and four Black-boxes that IHS used. 4) Propose the
formalized adversary model. And 5) Give the definition of judge.
Secondly, based on ROM of IHS, the security against known original
cover attack (KOCA-KOCA-security) is defined. Then, we give an
actual information hiding scheme and prove that it is
KOCA-KOCA-secure. Finally, we conclude the paper and propose the
open problems of further research.
Abstract: This paper presents Faults Forecasting System (FFS)
that utilizes statistical forecasting techniques in analyzing process
variables data in order to forecast faults occurrences. FFS is
proposing new idea in detecting faults. Current techniques used in
faults detection are based on analyzing the current status of the
system variables in order to check if the current status is fault or not.
FFS is using forecasting techniques to predict future timing for faults
before it happens. Proposed model is applying subset modeling
strategy and Bayesian approach in order to decrease dimensionality
of the process variables and improve faults forecasting accuracy. A
practical experiment, designed and implemented in Okayama
University, Japan, is implemented, and the comparison shows that
our proposed model is showing high forecasting accuracy and
BEFORE-TIME.
Abstract: In wireless sensor network (WSN) the use of mobile
sink has been attracting more attention in recent times. Mobile sinks
are more effective means of balancing load, reducing hotspot
problem and elongating network lifetime. The sensor nodes in WSN
have limited power supply, computational capability and storage and
therefore for continuous data delivery reliability becomes high
priority in these networks. In this paper, we propose a Reliable
Energy-efficient Data Dissemination (REDD) scheme for WSNs with
multiple mobile sinks. In this strategy, sink first determines the
location of source and then directly communicates with the source
using geographical forwarding. Every forwarding node (FN) creates a
local zone comprising some sensor nodes that can act as
representative of FN when it fails. Analytical and simulation study
reveals significant improvement in energy conservation and reliable
data delivery in comparison to existing schemes.
Abstract: This paper proposes new algorithms for the computeraided
design and manufacture (CAD/CAM) of 3D woven multi-layer
textile structures. Existing commercial CAD/CAM systems are often
restricted to the design and manufacture of 2D weaves. Those
CAD/CAM systems that do support the design and manufacture of
3D multi-layer weaves are often limited to manual editing of design
paper grids on the computer display and weave retrieval from stored
archives. This complex design activity is time-consuming, tedious
and error-prone and requires considerable experience and skill of a
technical weaver. Recent research reported in the literature has
addressed some of the shortcomings of commercial 3D multi-layer
weave CAD/CAM systems. However, earlier research results have
shown the need for further work on weave specification, weave
generation, yarn path editing and layer binding. Analysis of 3D
multi-layer weaves in this research has led to the design and
development of efficient and robust algorithms for the CAD/CAM of
3D woven multi-layer textile structures. The resulting algorithmically
generated weave designs can be used as a basis for lifting plans that
can be loaded onto looms equipped with electronic shedding
mechanisms for the CAM of 3D woven multi-layer textile structures.
Abstract: This paper presents a new spread-spectrum
watermarking algorithm for digital images in discrete wavelet
transform (DWT) domain. The algorithm is applied for embedding
watermarks like patient identification /source identification or
doctors signature in binary image format into host digital
radiological image for potential telemedicine applications.
Performance of the algorithm is analysed by varying the gain factor,
subband decomposition levels, and size of watermark. Simulation
results show that the proposed method achieves higher watermarking
capacity.
Abstract: The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy architecture based on Extended Kalman filter. To test the performance and applicability of the proposed neuro-fuzzy model, simulation study of nonlinear complex dynamic system is carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction of financial time series. A benchmark case studie is used to demonstrate that the proposed model is a superior neuro-fuzzy modeling technique.
Abstract: Real-time 3D applications have to guarantee
interactive rendering speed. There is a restriction for the number of
polygons which is rendered due to performance of a graphics hardware
or graphics algorithms. Generally, the rendering performance will be
drastically increased when handling only the dynamic 3d models,
which is much fewer than the static ones. Since shapes and colors of
the static objects don-t change when the viewing direction is fixed, the
information can be reused. We render huge amounts of polygon those
cannot handled by conventional rendering techniques in real-time by
using a static object image and merging it with rendering result of the
dynamic objects. The performance must be decreased as a
consequence of updating the static object image including removing
an static object that starts to move, re-rending the other static objects
being overlapped by the moving ones. Based on visibility of the object
beginning to move, we can skip the updating process. As a result, we
enhance rendering performance and reduce differences of rendering
speed between each frame. Proposed method renders total
200,000,000 polygons that consist of 500,000 dynamic polygons and
the rest are static polygons in about 100 frames per second.
Abstract: In the current research, we present an operation framework and protection mechanism to facilitate secure environment to protect mobile agents against tampering. The system depends on the presence of an authentication authority. The advantage of the proposed system is that security measures is an integral part of the design, thus common security retrofitting problems do not arise. This is due to the presence of AlGamal encryption mechanism to protect its confidential content and any collected data by the agent from the visited host . So that eavesdropping on information from the agent is no longer possible to reveal any confidential information. Also the inherent security constraints within the framework allow the system to operate as an intrusion detection system for any mobile agent environment. The mechanism is tested for most of the well known severe attacks against agents and networked systems. The scheme proved a promising performance that makes it very much recommended for the types of transactions that needs highly secure environments, e. g., business to business.
Abstract: Global Solar Radiation (H) for Dubai and Sharjah,
Latitude 25.25oN, Longitude 55oE and 25.29oN, Longitude 55oE
respectively have been studied using sunshine hour data (n) of the
areas using various methods. These calculated global solar radiation
values are then compared to the measured values presented by
NASA. Furthermore, the extraterrestrial (H0), diffuse (Hd) and beam
radiation (Hb) are also calculated. The diffuse radiation is calculated
using methods proposed by Page and Liu and Jordan (L-J). Diffuse
Radiation from the Page method is higher than the L-J method.
Moreover, the clearness index (KT) signifies a clear sky almost all
year round. Rainy days are hardly a few in a year and limited in the
months December to March. The temperature remains between 25oC
in winter to 44oC in summer and is desirable for thermal applications
of solar energy. From the estimated results, it appears that solar
radiation can be utilized very efficiently throughout the year for
photovoltaic and thermal applications.
Abstract: The authors present a mixed method for reducing the order of the large-scale dynamic systems. In this method, the denominator polynomial of the reduced order model is obtained by using the modified pole clustering technique while the coefficients of the numerator are obtained by Pade approximations. This method is conceptually simple and always generates stable reduced models if the original high-order system is stable. The proposed method is illustrated with the help of the numerical examples taken from the literature.
Abstract: After allowing direct flights from Mainland China to
Taiwan, Chinese tourists increased according to Tourism
Bureaustatistics. There are from 0.19 to 2 million tourists from 2008 to
2011. Mainland China has become the main source of Taiwan
developing tourism industry. Taiwanese government should know
more about comments from Chinese tourists to Taiwan in order
toproperly market Taiwan tourism and enhance the overall quality of
tourism. In order to understand Chinese visitors’ comments, this study
adopts content analysis to analyze electronic word-of-mouth on Web.
This study collects 375 blog articles of Chinese tourists from
Ctrip.com as a database during 2009 to 2011. Through the qualitative
data analysis the traveling destination imagesis divided into seven
dimensions, such as senic spots, shopping, food and beverages,
accommodations, transportation, festivals and recreation activities.
Finally, this study proposes some practical managerial implication to
know both positive and negative images of the seven dimensions from
Chinese tourists, providing marketing strategies and suggestions to
traveling agency industry.
Abstract: In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for power system. The dependence of load on several factors makes the load forecasting a very challenging job. An over estimation of the load may cause premature investment and unnecessary blocking of the capital where as under estimation of load may result in shortage of equipment and circuits. It is always better to plan the system for the load slightly higher than expected one so that no exigency may arise. In this paper, a load-forecasting model is proposed using a multilayer neural network with an appropriately modified back propagation learning algorithm. Once the neural network model is designed and trained, it can forecast the load of the power system 24 hours ahead on daily basis and can also forecast the cumulative load on daily basis. The real load data that is used for the Artificial Neural Network training was taken from LDC, Gujarat Electricity Board, Jambuva, Gujarat, India. The results show that the load forecasting of the ANN model follows the actual load pattern more accurately throughout the forecasted period.
Abstract: In this paper, we propose a novel adaptive voltage control strategy for boost converter via Inverse LQ Servo-Control. Our presented strategy is based on an analytical formula of Inverse Linear Quadratic (ILQ) design method, which is not necessary to solve Riccati’s equation directly. The optimal and adaptive controller of the voltage control system is designed. The stability and the robust control are analyzed. Whereas, we can get the analytical solution for the optimal and robust voltage control is achieved through the natural angular velocity within a single parameter and we can change the responses easily via the ILQ control theory. Our method provides effective results as the stable responses and the response times are not drifted even if the condition is changed widely.
Abstract: The inability to implement the principles of good
corporate governance (GCG) as demonstrated in the surveys is due to
a number of constraints which can be classified into three; namely internal constraints, external constraints, and constraints coming
from the structure of ownership. The issues in the internal constraints
mentioned are related to the function of several elements of the company. As a business organization, corporation is unable to
achieve its goal to successfully implement GCG principles since it is
not support by its internal elements- functions. Two of several numbers of internal elements of a company are ethical work climate
and leadership style of the top management.
To prove the correlation between internal function of organization
(in this case ethical work climate and transformational leadership)
and the successful implementation of GCG principles, this study
proposes two hypotheses to be empirically tested on thirty surveyed organizations; eleven of which are state-owned companies and
nineteen are private companies. These thirty corporations are listed in
the Jakarta Stock Exchange. All state-owned companies in the
samples are those which have been privatized.
The research showed that internal function of organization give
support to the successful implementation of GCG principle. In this
research we can prove that : (i) ethical work climate has positive
significance of correlation with the successful implementation of
social awareness principle (one of principles on GCG) and, (ii) only
at the state-owned companies, transformational leadership have
positive significance effect to forming the ethical work climate.
Abstract: Proteins levels produced by bacteria may be increased
in stressful surroundings, such as in the presence of antibiotics. It
appears that many antimicrobial agents or antibiotics, when used at
low concentrations, have in common the ability to activate or repress
gene transcription, which is distinct from their inhibitory effect.
There have been comparatively few studies on the potential of
antibiotics or natural compounds in nature as a specific chemical
signal that can trigger a variety of biological functions. Therefore,
this study was focusing on the effect of essential oils from
Cymbopogon flexuosus and C. nardus in regulating proteins
production by Bacillus subtilis ATCC 21332. The Minimum
Inhibition Concentrations (MICs) of both essential oils on B. subtilis
were determined by using microdilution assay, resulting 0.2% and
1.56% for each C. flexuosus and C. nardus subsequently. The
bacteria were further exposed to each essential oils at concentration
of 0.01XMIC for 2 days. The proteins were then isolated and
analyzed by sodium dodecyl sulfate polyacrylamide gel
electrophoresis (SDS-PAGE). Protein profile showed that a band
with approximate size of 250 kD was appeared for the treated
bacteria with essential oils. Thus, Bacillus subtilis ATCC 21332 in
stressful condition with the presence of essential oils at low
concentration could induce the protein production.
Abstract: Environmental awareness and depletion of the
petroleum resources are among vital factors that motivate a number
of researchers to explore the potential of reusing natural fiber as an
alternative composite material in industries such as packaging,
automotive and building constructions. Natural fibers are available in
abundance, low cost, lightweight polymer composite and most
importance its biodegradability features, which often called “ecofriendly"
materials. However, their applications are still limited due
to several factors like moisture absorption, poor wettability and large
scattering in mechanical properties. Among the main challenges on
natural fibers reinforced matrices composite is their inclination to
entangle and form fibers agglomerates during processing due to
fiber-fiber interaction. This tends to prevent better dispersion of the
fibers into the matrix, resulting in poor interfacial adhesion between
the hydrophobic matrix and the hydrophilic reinforced natural fiber.
Therefore, to overcome this challenge, fiber treatment process is one
common alternative that can be use to modify the fiber surface
topology by chemically, physically or mechanically technique.
Nevertheless, this paper attempt to focus on the effect of
mercerization treatment on mechanical properties enhancement of
natural fiber reinforced composite or so-called bio composite. It
specifically discussed on mercerization parameters, and natural fiber
reinforced composite mechanical properties enhancement.
Abstract: Communication is an important factor and a prop in
directing corporate activities efficiently, in ensuring the flow of
knowledge which is necessary for the continuity of the institution, in
creating a common language in the institution, in transferring
corporate culture and ultimately in corporate success. The idea of
transmitting the knowledge among the workers in a healthy manner
has revived knowledge communication. Knowledge communication
can be defined as the act of mutual creation and communication of
intuitions, assessments, experiences and capabilities, as long as
maintained effectively, can provide advantages such as corporate
continuity, access to corporate objectives and making true
administrative decisions. Although the benefits of the knowledge
communication to corporations are known, and the necessary worth
and care is given, some hardships may arise which makes it difficult
or even block it. In this article, difficulties that prevent knowledge
communication will be discussed and solutions will be proposed.