Abstract: H.264/AVC offers a considerably higher improvement
in coding efficiency compared to other compression standards such
as MPEG-2, but computational complexity is increased significantly.
In this paper, we propose selective mode decision schemes for fast
intra prediction mode selection. The objective is to reduce the
computational complexity of the H.264/AVC encoder without
significant rate-distortion performance degradation. In our proposed
schemes, the intra prediction complexity is reduced by limiting the
luma and chroma prediction modes using the directional information
of the 16×16 prediction mode. Experimental results are presented to
show that the proposed schemes reduce the complexity by up to 78%
maintaining the similar PSNR quality with about 1.46% bit rate
increase in average.
Abstract: In this paper an open agent-based modular framework
for personalized and adaptive curriculum generation in e-learning
environment is proposed. Agent-based approaches offer several
potential advantages over alternative approaches. Agent-based
systems exhibit high levels of flexibility and robustness in dynamic
or unpredictable environments by virtue of their intrinsic autonomy.
The presented framework enables integration of different types of
expert agents, various kinds of learning objects and user modeling
techniques. It creates possibilities for adaptive e-learning process.
The KM e-learning system is in a process of implementation in
Varna Free University and will be used for supporting the
educational process at the University.
Abstract: The Malaysian government is promoting
entrepreneurship development skills amongst farmers through informal
courses. These courses will concentrate on teaching managerial skills as
inevitable means for small farms to succeed by making farmers more
creative and innovative. Therefore it is important to assess the effect of
informal agri-entrepreneurial training in developing entrepreneurship
among the farmers in Malaysia. Seven hundred and ninety six farmers
(796) farmers were interviewed via structured questionnaire to define
their opinion on whether the current informal educational and training
establishments are sufficient to teach and develop entrepreneurial
skills. Factor analysis and logic regression analysis were used to
determine the motivating factors and predict their impact on the
development of entrepreneurial skills. The result from the factor analysis
led us to investigate the association between these factors and farmers-
opinions about the development of entrepreneurial skills and traits
through participating in informal entrepreneurship training or education.
The outcome has shown us that the importance of informal training to
promote entrepreneurship among farmers is crucial. The training should
be intensified to encourage farmers to not only focus on the modern
technologies but also on the fundamental changes in their attitude towards
agriculture as a business.
DOA:
KMO: Kaiser- Meyer- Olkin Test
MOA: Ministry of Agriculture
NMP: Ninth Malaysia Plan
NAP: Third National Agricultural Policy (2000-2010)
Abstract: This study aimed at developing a forecasting model on the number of Dengue Haemorrhagic Fever (DHF) incidence in Northern Thailand using time series analysis. We developed Seasonal Autoregressive Integrated Moving Average (SARIMA) models on the data collected between 2003-2006 and then validated the models using the data collected between January-September 2007. The results showed that the regressive forecast curves were consistent with the pattern of actual values. The most suitable model was the SARIMA(2,0,1)(0,2,0)12 model with a Akaike Information Criterion (AIC) of 12.2931 and a Mean Absolute Percent Error (MAPE) of 8.91713. The SARIMA(2,0,1)(0,2,0)12 model fitting was adequate for the data with the Portmanteau statistic Q20 = 8.98644 ( x20,95= 27.5871, P>0.05). This indicated that there was no significant autocorrelation between residuals at different lag times in the SARIMA(2,0,1)(0,2,0)12 model.
Abstract: Artificial Neural Network (ANN)s can be modeled for
High Energy Particle analysis with special emphasis on shower core
location. The work describes the use of an ANN based system which
has been configured to predict locations of cores of showers in the
range 1010.5 to 1020.5 eV. The system receives density values as
inputs and generates coordinates of shower events recorded for values
captured by 20 core positions and 80 detectors in an area of 100
meters. Twenty ANNs are trained for the purpose and the positions
of shower events optimized by using cooperative ANN learning. The
results derived with variations of input upto 50% show success rates
in the range of 90s.
Abstract: Saccharomyces cerevisiae (baker-s yeast) can exhibit
sustained oscillations during the operation in a continuous bioreactor
that adversely affects its stability and productivity. Because of
heterogeneous nature of cell populations, the cell population balance
models can be used to capture the dynamic behavior of such cultures.
In this paper an unstructured, segregated model is used which is
based on population balance equation(PBE) and then in order to
simulation, the 4th order Rung-Kutta is used for time dimension and
three methods, finite difference, orthogonal collocation on finite
elements and Galerkin finite element are used for discretization of the
cell mass domain. The results indicate that the orthogonal collocation
on finite element not only is able to predict the oscillating behavior of
the cell culture but also needs much little time for calculations.
Therefore this method is preferred in comparison with other methods.
In the next step two controllers, a globally linearizing control (GLC)
and a conventional proportional-integral (PI) controller are designed
for controlling the total cell mass per unit volume, and performances
of these controllers are compared through simulation. The results
show that although the PI controller has simpler structure, the GLC
has better performance.
Abstract: Environmental impact assessment (EIA) is a procedure tool of environmental management for identifying, predicting, evaluating and mitigating the adverse effects of development proposals. EIA reports usually analyze how the amounts or concentrations of pollutants obey the relevant standards. Actually, many analytical tools can deepen the analysis of environmental impacts in EIA reports, such as life cycle assessment (LCA) and environmental risk assessment (ERA). Life cycle impact assessment (LCIA) is one of steps in LCA to introduce the causal relationships among environmental hazards and damage. Incorporating the LCIA concept into ERA as an integrated tool for EIA can extend the focus of the regulatory compliance of environmental impacts to determine of the significance of environmental impacts. Sometimes, when using integrated tools, it is necessary to consider fuzzy situations due to insufficient information; therefore, ERA should be generalized to fuzzy risk assessment (FRA). Finally, the use of the proposed methodology is demonstrated through the study case of the expansion plan of the world-s largest plastics processing factory.
Abstract: A zero dimensional model has been used to investigate
the combustion performance of a single cylinder direct injection
diesel engine fueled by biofuels with options like supercharging and
exhaust gas recirculation. The numerical simulation was performed at
constant speed. The indicated pressure, temperature diagrams are
plotted and compared for different fuels. The emissions of soot and
nitrous oxide are computed with phenomenological models. The
experimental work was also carried out with biodiesel (palm stearin
methyl ester) diesel blends, ethanol diesel blends to validate
simulation results with experimental results, and observed that the
present model is successful in predicting the engine performance with
biofuels.
Abstract: This paper describes an automated event detection and location system for water distribution pipelines which is based upon low-cost sensor technology and signature analysis by an Artificial
Neural Network (ANN). The development of a low cost failure sensor which measures the opacity or cloudiness of the local water
flow has been designed, developed and validated, and an ANN based system is then described which uses time series data produced by
sensors to construct an empirical model for time series prediction and
classification of events. These two components have been installed,
tested and verified in an experimental site in a UK water distribution
system. Verification of the system has been achieved from a series of
simulated burst trials which have provided real data sets. It is concluded that the system has potential in water distribution network
management.
Abstract: Data compression is used operationally to reduce bandwidth and storage requirements. An efficient method for achieving lossless weather radar data compression is presented. The characteristics of the data are taken into account and the optical linear prediction is used for the PPI images in the weather radar data in the proposed method. The next PPI image is identical to the current one and a dramatic reduction in source entropy is achieved by using the prediction algorithm. Some lossless compression methods are used to compress the predicted data. Experimental results show that for the weather radar data, the method proposed in this paper outperforms the other methods.
Abstract: This paper presents a method for determining the
uniaxial tensile properties such as Young-s modulus, yield strength
and the flow behaviour of a material in a virtually non-destructive
manner. To achieve this, a new dumb-bell shaped miniature
specimen has been designed. This helps in avoiding the removal of
large size material samples from the in-service component for the
evaluation of current material properties. The proposed miniature
specimen has an advantage in finite element modelling with respect
to computational time and memory space. Test fixtures have been
developed to enable the tension tests on the miniature specimen in a
testing machine. The studies have been conducted in a chromium
(H11) steel and an aluminum alloy (AR66). The output from the
miniature test viz. load-elongation diagram is obtained and the finite
element simulation of the test is carried out using a 2D plane stress
analysis. The results are compared with the experimental results. It is
observed that the results from the finite element simulation
corroborate well with the miniature test results. The approach seems
to have potential to predict the mechanical properties of the
materials, which could be used in remaining life estimation of the
various in-service structures.
Abstract: Bioinformatics and computational biology involve
the use of techniques including applied mathematics,
informatics, statistics, computer science, artificial intelligence,
chemistry, and biochemistry to solve biological problems
usually on the molecular level. Research in computational
biology often overlaps with systems biology. Major research
efforts in the field include sequence alignment, gene finding,
genome assembly, protein structure alignment, protein structure
prediction, prediction of gene expression and proteinprotein
interactions, and the modeling of evolution. Various
global rearrangements of permutations, such as reversals and
transpositions,have recently become of interest because of their
applications in computational molecular biology. A reversal is
an operation that reverses the order of a substring of a permutation.
A transposition is an operation that swaps two adjacent
substrings of a permutation. The problem of determining the
smallest number of reversals required to transform a given
permutation into the identity permutation is called sorting by
reversals. Similar problems can be defined for transpositions
and other global rearrangements. In this work we perform a
study about some genome rearrangement primitives. We show
how a genome is modelled by a permutation, introduce some
of the existing primitives and the lower and upper bounds
on them. We then provide a comparison of the introduced
primitives.
Abstract: The problem of Small Area Estimation (SAE) is complex because of various information sources and insufficient data. In this paper, an approach for SAE is presented for decision-making at national, regional and local level. We propose an Empirical Best Linear Unbiased Predictor (EBLUP) as an estimator in order to combine several information sources to evaluate various indicators. First, we present the urban audit project and its environmental, social and economic indicators. Secondly, we propose an approach for decision making in order to estimate indicators. An application is used to validate the theoretical proposal. Finally, a decision support system is presented based on open-source environment.
Abstract: The objective of this paper is to design a model of human vital sign prediction for decreasing prediction error by using two states mapping based time series neural network BP (back-propagation) model. Normally, lot of industries has been applying the neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has a residual error between real value and prediction output. Therefore, we designed two states of neural network model for compensation of residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We found that most of simulations cases were satisfied by the two states mapping based time series prediction model compared to normal BP. In particular, small sample size of times series were more accurate than the standard MLP model. We expect that this algorithm can be available to sudden death prevention and monitoring AGENT system in a ubiquitous homecare environment.
Abstract: The paper presents an applied study of a multivariate AR(p) process fitted to daily data from U.S. commodity futures markets with the use of Bayesian statistics. In the first part a detailed description of the methods used is given. In the second part two BVAR models are chosen one with assumption of lognormal, the second with normal distribution of prices conditioned on the parameters. For a comparison two simple benchmark models are chosen that are commonly used in todays Financial Mathematics. The article compares the quality of predictions of all the models, tries to find an adequate rate of forgetting of information and questions the validity of Efficient Market Hypothesis in the semi-strong form.
Abstract: Snow cover is an important phenomenon in
hydrology, hence modeling the snow accumulation and melting is an
important issue in places where snowmelt significantly contributes to
runoff and has significant effect on water balance. The physics-based
models are invariably distributed, with the basin disaggregated into
zones or grid cells. Satellites images provide valuable data to verify
the accuracy of spatially distributed model outputs. In this study a
spatially distributed physically based model (WetSpa) was applied to
predict snow cover and melting in the Latyan dam watershed in Iran.
Snowmelt is simulated based on an energy balance approach. The
model is applied and calibrated with one year of observed daily
precipitation, air temperature, windspeed, and daily potential
evaporation. The predicted snow-covered area is compared with
remotely sensed images (MODIS). The results show that simulated
snow cover area SCA has a good agreement with satellite image
snow cover area SCA from MODIS images. The model performance
is also tested by statistical and graphical comparison of simulated and
measured discharges entering the Latyan dam reservoir.
Abstract: Helical milling operations are used to generate or
enlarge boreholes by means of a milling tool. The bore diameter can be
adjusted through the diameter of the helical path. The kinematics of
helical milling on a three axis machine tool is analysed firstly. The
relationships between processing parameters, cutting tool geometry
characters with machined hole feature are formulated. The feed motion
of the cutting tool has been decomposed to plane circular feed and
axial linear motion. In this paper, the time varying cutting forces acted
on the side cutting edges and end cutting edges of the flat end cylinder
miller is analysed using a discrete method separately. These two
components then are combined to produce the cutting force model
considering the complicated interaction between the cutters and
workpiece. The time varying cutting force model describes the
instantaneous cutting force during processing. This model could be
used to predict cutting force, calculate statics deflection of cutter and
workpiece, and also could be the foundation of dynamics model and
predicting chatter limitation of the helical milling operations.
Abstract: This research were investigated, determined, and
analyzed of the climate characteristically change in the provincial
Udon Thani in the period of 60 surrounding years from 1951 to 2010
A.D. that it-s transferred to effects of climatologically data for
determining global warming. Statistically significant were not found
for the 60 years- data (R2
Abstract: This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW Photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three Radial Basis Function Neural Networks (RBFNN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated RBFNN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and non-linear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network.
Abstract: The majority of existing predictors for time series are
model-dependent and therefore require some prior knowledge for the
identification of complex systems, usually involving system
identification, extensive training, or online adaptation in the case of
time-varying systems. Additionally, since a time series is usually
generated by complex processes such as the stock market or other
chaotic systems, identification, modeling or the online updating of
parameters can be problematic. In this paper a model-free predictor
(MFP) for a time series produced by an unknown nonlinear system or
process is derived using tracking theory. An identical derivation of the
MFP using the property of the Newton form of the interpolating
polynomial is also presented. The MFP is able to accurately predict
future values of a time series, is stable, has few tuning parameters and
is desirable for engineering applications due to its simplicity, fast
prediction speed and extremely low computational load. The
performance of the proposed MFP is demonstrated using the
prediction of the Dow Jones Industrial Average stock index.