Abstract: This paper introduces the foundations of Bayesian probability theory and Bayesian decision method. The main goal of Bayesian decision theory is to minimize the expected loss of a decision or minimize the expected risk. The purposes of this study are to review the decision process on the issue of flood occurrences and to suggest possible process for decision improvement. This study examines the problem structure of flood occurrences and theoretically explicates the decision-analytic approach based on Bayesian decision theory and application to flood occurrences in Environmental Engineering. In this study, we will discuss about the flood occurrences upon an annual maximum water level in cm, 43-year record available from 1965 to 2007 at the gauging station of Sagaing on the Ayeyarwady River with the drainage area - 120193 sq km by using Bayesian decision method. As a result, we will discuss the loss and risk of vast areas of agricultural land whether which will be inundated or not in the coming year based on the two standard maximum water levels during 43 years. And also we forecast about that lands will be safe from flood water during the next 10 years.
Abstract: System MEMORI automatically detects and recognizes
rotated and/or rescaled versions of the objects of a database within
digital color images with cluttered background. This task is accomplished
by means of a region grouping algorithm guided by heuristic
rules, whose parameters concern some geometrical properties and the
recognition score of the database objects. This paper focuses on the
strategies implemented in MEMORI for the estimation of the heuristic
rule parameters. This estimation, being automatic, makes the system
a self configuring and highly user-friendly tool.
Abstract: In this paper a combination approach of two heuristic-based algorithms: genetic algorithm and tabu search is proposed. It has been developed to obtain the least cost based on the split-pipe design of looped water distribution network. The proposed combination algorithm has been applied to solve the three well-known water distribution networks taken from the literature. The development of the combination of these two heuristic-based algorithms for optimization is aimed at enhancing their strengths and compensating their weaknesses. Tabu search is rather systematic and deterministic that uses adaptive memory in search process, while genetic algorithm is probabilistic and stochastic optimization technique in which the solution space is explored by generating candidate solutions. Split-pipe design may not be realistic in practice but in optimization purpose, optimal solutions are always achieved with split-pipe design. The solutions obtained in this study have proved that the least cost solutions obtained from the split-pipe design are always better than those obtained from the single pipe design. The results obtained from the combination approach show its ability and effectiveness to solve combinatorial optimization problems. The solutions obtained are very satisfactory and high quality in which the solutions of two networks are found to be the lowest-cost solutions yet presented in the literature. The concept of combination approach proposed in this study is expected to contribute some useful benefits in diverse problems.
Abstract: The Indian subcontinent is facing a massive challenge with regards to the energy security in member countries, i.e. providing a reliable source of electricity to facilitate development across various sectors of the economy and thereby achieve the developmental targets it has set for itself. A highly precarious situation exists in the subcontinent which is observed in the series of system failures which most of the times leads to system collapses-blackouts. To mitigate the issues related with energy security as well as keep in check the increasing supply demand gap, a possible solution that stands in front of the subcontinent is the deployment of an interconnected electricity ‘Supergrid’ designed to carry huge quanta of power across the sub continent as well as provide the infra structure for RES integration. This paper assesses the need and conditions for a Supergrid deployment and consequently proposes a meshed topology based on VSC HVDC converters for the Supergrid modeling.
Abstract: Massive use of places with strong tourist attraction
with the consequent possibility of losing place-identity produces
harmful effects on cities and their users. In order to mitigate this risk,
areas close to such places can be identified so as to widen the
visitor-s range of action and offer alternative activities integrated
with the main site. The cultural places and appropriate activities can
be identified using a method of analysis and design able to trace the
identity of the places, their characteristics and potential, and to
provide a sustainable improvement. The aim of this work is to
propose PlaceMaker as a method of urban analysis and design which
both detects elements that do not feature in traditional mapping and
which constitute the contemporary identity of the places, and
identifies appropriate project interventions. Two final complex maps
– the first of analysis and the second of design – respectively
represent the identity of places and project interventions. In order to
illustrate the method-s potential; the results of the experimentation
carried out in the Trevi-Pantheon route in Rome and the appropriate
interventions to decongest the area are illustrated.
Abstract: This paper presents a new growing neural network for
cluster analysis and market segmentation, which optimizes the size
and structure of clusters by iteratively checking them for multivariate
normality. We combine the recently published SGNN approach [8]
with the basic principle underlying the Gaussian-means algorithm
[13] and the Mardia test for multivariate normality [18, 19]. The new
approach distinguishes from existing ones by its holistic design and
its great autonomy regarding the clustering process as a whole. Its
performance is demonstrated by means of synthetic 2D data and by
real lifestyle survey data usable for market segmentation.
Abstract: According to the new developments in the field of information and communication technologies, the necessity arises for active use of these new technologies in education. It is clear that the integration of technology in education system will be different for primary-higher education or traditional- distance education. In this study, the subject of the integration of technology for distance education was discussed. The subject was taken from the viewpoint of students. With using the information of student feedback about education program in which new technological medias are used, how can survey variables can be separated into the factors as positive, negative and supporter and how can be redesigned education strategy of the higher education associations with the examining the variables of each determinated factor is explained. The paper concludes with the recommendations about the necessitity of working as a group of different area experts and using of numerical methods in establishing of education strategy to be successful.
Abstract: Industrial robots become useless without end-effectors
that for many instances are in the form of friction grippers.
Commonly friction grippers apply frictional forces to different
objects on the basis of programmers- experiences. This puts a
limitation on the effectiveness of gripping force that may result in
damaging the object. This paper describes various stages of design
and development of a low cost sensor-based robotic gripper that
would facilitate the task of applying right gripping forces to different
objects. The gripper is also equipped with range sensors in order to
avoid collisions of the gripper with objects. It is a fully functional
automated pick and place gripper which can be used in many
industrial applications. Yet it can also be altered or further developed
in order to suit a larger number of industrial activities. The current
design of gripper could lead to designing completely automated robot
grippers able to improve the efficiency and productivity of industrial
robots.
Abstract: The search for factors that influence user behavior has remained an important theme for both the academic and practitioner Information Systems Communities. In this paper we examine relevant user behaviors in the phase after adoption and investigate two factors that are expected to influence such behaviors, namely User Involvement (UI) and Personal Innovativeness in IT (PIIT). We conduct a field study to examine how these factors influence postadoption behavior and how they are interrelated. Building on theoretical premises and prior empirical findings, we propose and test two alternative models of the relationship between these factors. Our results reveal that the best explanation of post-adoption behavior is provided by the model where UI and PIIT independently influence post-adoption behavior. Our findings have important implications for research and practice. To that end, we offer directions for future research.
Abstract: In this paper, a target signal detection method using
multiple signal classification (MUSIC) algorithm is proposed. The
MUSIC algorithm is a subspace-based direction of arrival (DOA)
estimation method. The algorithm detects the DOAs of multiple
sources using the inverse of the eigenvalue-weighted eigen spectra. To
apply the algorithm to target signal detection for GSC-based
beamforming, we utilize its spectral response for the target DOA in
noisy conditions. For evaluation of the algorithm, the performance of
the proposed target signal detection method is compared with that of
the normalized cross-correlation (NCC), the fixed beamforming, and
the power ratio method. Experimental results show that the proposed
algorithm significantly outperforms the conventional ones in receiver
operating characteristics(ROC) curves.
Abstract: This article proposes a current-mode square-rooting
circuit using current follower transconductance amplifier (CTFA).
The amplitude of the output current can be electronically controlled
via input bias current with wide input dynamic range. The proposed
circuit consists of only single CFTA. Without any matching
conditions and external passive elements, the circuit is then
appropriate for an IC architecture. The magnitude of the output signal
is temperature-insensitive. The PSpice simulation results are
depicted, and the given results agree well with the theoretical
anticipation. The power consumption is approximately 1.96mW at
±1.5V supply voltages.
Abstract: Extensive wind tunnel tests have been conducted to
investigate the unsteady flow field over and behind a 2D model of a
660 kW wind turbine blade section in pitching motion. The surface
pressure and wake dynamic pressure variation at a distance of 1.5
chord length from trailing edge were measured by pressure
transducers during several oscillating cycles at 3 reduced frequencies
and oscillating amplitudes. Moreover, form drag and linear
momentum deficit are extracted and compared at various conditions.
The results show that the wake velocity field and surface pressure of
the model have similar behavior before and after the airfoil beyond
the static stall angle of attack. In addition, the effects of reduced
frequency and oscillation amplitudes are discussed.
Abstract: The amount and heterogeneity of data in biomedical research, notably in interdisciplinary research, requires new methods for the collection, presentation and analysis of information. Important data from laboratory experiments as well as patient trials are available but come out of distributed resources. The Charite Medical School in Berlin has established together with the German Research Foundation (DFG) a new information service center for kidney diseases and transplantation (Open European Nephrology Science Centre - OpEN.SC). The system is based on a service-oriented architecture (SOA) with main and auxiliary modules arranged in four layers. To improve the reuse and efficient arrangement of the services the functionalities are described as business processes using the standardised Business Process Execution Language (BPEL).
Abstract: The pigments covered by film-forming polymers have
opened a prospect to improve the quality of water-based printing
inks. In this study such pigments were prepared by the initiated
polymerization of styrene and methacrylate derivative monomers in
the aqueous pigment dispersions. The formation of polymer films
covering pigment cores depends on the polymerization time and the
ratio of pigment to monomers. At the time of 4 hours and the ratio of
1/10 almost pigment particles are coated by the polymer. The formed
polymer covers of pigments have the average thickness of 5.95 nm.
The size increasing percentage of the coated particles after a week is
4.5 %, about fourteen-fold lower than of the original ones. The
obtained results indicate that the coated pigments are improved
dispersion stability in water medium along with a guarantee for the
optical colour.
Abstract: Academics and researchers are interested in the effects of social media on college students, with a specific focus on the most popular social media website; Facebook. Previous studied have found contradictory result on the relationship between Facebook usage and the student engagement with positive, detrimental and no significant relationships. However, these studies were limited to western higher education system. This paper fills a gap in the literature by using a sample (300) of Sri Lankan management undergraduates to examine the relationship between Facebook usage and student engagement. Student engagement was measured 35 item scale based on the National Survey of Student Engagement and Facebook usage by Facebook intensity scale. Descriptive statistics, path analysis and structural equation modeling were applied as statistical tools and techniques. Results indicate that student engagement scale was significantly negatively related with the Facebook usage with the influence from student engagement on Facebook usage.
Abstract: Nowadays, precipitation prediction is required for proper planning and management of water resources. Prediction with neural network models has received increasing interest in various research and application domains. However, it is difficult to determine the best neural network architecture for prediction since it is not immediately obvious how many input or hidden nodes are used in the model. In this paper, neural network model is used as a forecasting tool. The major aim is to evaluate a suitable neural network model for monthly precipitation mapping of Myanmar. Using 3-layerd neural network models, 100 cases are tested by changing the number of input and hidden nodes from 1 to 10 nodes, respectively, and only one outputnode used. The optimum model with the suitable number of nodes is selected in accordance with the minimum forecast error. In measuring network performance using Root Mean Square Error (RMSE), experimental results significantly show that 3 inputs-10 hiddens-1 output architecture model gives the best prediction result for monthly precipitation in Myanmar.
Abstract: This paper presents a narrative management system
for organizations to capture organization's tacit knowledge
through stories. The intention of capturing tacit knowledge is to
address the problem that comes with the mobility of workforce in
organisation. Storytelling in knowledge management context is
seen as a powerful management tool to communicate tacit
knowledge in organization. This narrative management system is
developed firstly to enable uploading of many types of knowledge
sharing stories, from general to work related-specific stories and
secondly, each video has comment functionality where knowledge
users can post comments to other knowledge users. The narrative
management system allows the stories to browse, search and view
by the users. In the system, stories are stored in a video repository.
Stories that were produced from this framework will improve
learning, knowledge transfer facilitation and tacit knowledge
quality in an organization.
Abstract: In this paper, a block code to minimize the peak-toaverage
power ratio (PAPR) of orthogonal frequency division
multiplexing (OFDM) signals is proposed. It is shown that cyclic
shift and codeword inversion cause not change to peak envelope
power. The encoding rule for the proposed code comprises of
searching for a seed codeword, shifting the register elements, and
determining codeword inversion, eliminating the look-up table for
one-to-one correspondence between the source and the coded data.
Simulation results show that OFDM systems with the proposed code
always have the minimum PAPR.
Abstract: Our objectives were to evaluate the effects of sire
breed, type of protein supplement, level of supplementation and sex
on wool spinning fineness (SF), its correlations with other wool
characteristics and prediction accuracy in F1 Merino crossbred lambs.
Texel, Coopworth, White Suffolk, East Friesian and Dorset rams
were mated with 500 purebred Merino dams at a ratio of 1:100 in
separate paddocks within a single management system. The F1
progeny were raised on ryegrass pasture until weaning, before forty
lambs were randomly allocated to treatments in a 5 x 2 x 2 x 2
factorial experimental design representing 5 sire breeds, 2
supplementary feeds (canola or lupins), 2 levels of supplementation
(1% or 2% of liveweight) and sex (wethers or ewes). Lambs were
supplemented for six weeks after an initial three weeks of adjustment,
wool sampled at the commencement and conclusion of the feeding
trial and analyzed for SF, mean fibre diameter (FD), coefficient of
variation (CV), standard deviation, comfort factor (CF), fibre
curvature (CURV), and clean fleece yield. Data were analyzed using
mixed linear model procedures with sire fitted as a random effect,
and sire breed, sex, supplementary feed type, level of
supplementation and their second-order interactions as fixed effects.
Sire breed (P
Abstract: Analysis and visualization of microarraydata is veryassistantfor biologists and clinicians in the field of diagnosis and treatment of patients. It allows Clinicians to better understand the structure of microarray and facilitates understanding gene expression in cells. However, microarray dataset is a complex data set and has thousands of features and a very small number of observations. This very high dimensional data set often contains some noise, non-useful information and a small number of relevant features for disease or genotype. This paper proposes a non-linear dimensionality reduction algorithm Local Principal Component (LPC) which aims to maps high dimensional data to a lower dimensional space. The reduced data represents the most important variables underlying the original data. Experimental results and comparisons are presented to show the quality of the proposed algorithm. Moreover, experiments also show how this algorithm reduces high dimensional data whilst preserving the neighbourhoods of the points in the low dimensional space as in the high dimensional space.