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Jumlah kadar kelahiran kasar, kadar kematian kasar dan kadar kesuburan bagi negara terpilih
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Prediction of water pollution concentration transport using finite difference methods
No full textMaster of Science in Engineering MathematicsWater is an important component of the earth. Human being and living organisms are not only demand for the quantity of water but also the quality of the water. Human activities nowadays is one of the causes of the water pollution. The pollution give bad effect to the physical and characteristic of water contents. It is not practical to monitor all aspects of water flow and transport distribution. So, in order to help people to access the polluted area, prediction of water pollution concentration must be modelled. This study proposed a one dimensional advection diffusion equation for predicting the water pollution concentration transport. In order to solve the advection diffusion equation, the discretization process has been conducted using the explicit forward-time central-space method and implicit Crank Nicolson method. A problem in water pollution concentration distribution has been used in validating the explicit forward-time central-space method and the implicit Crank Nicolson method. After the validation process, the numerical algorithms have been developed in MATLAB software in order to solve one dimensional advection diffusion equation for water pollution concentration. For the purpose of the simulation, the initial and boundary conditions, the spatial steps and time steps as well as the advection diffusion equation in finite difference form have been encoded. The numerical results of one dimensional advection diffusion equation had successfully predicted the transportation of water pollution concentration by manipulating the velocity and diffusion parameters. Therefore, these two parameters give effect towards the speed of the pollutant transport and concentration of pollutant travel at certain distance when the pollution happen
Maximum power point tracking (MPPT) algorithm for photovoltaic (PV) system based on parabolic prediction method
No full textMaster of Science in Electrical System EngineeringThe increased consumption of electric energy during the recent decades has
prompted a search for other sources of energy. One of these sources is solar energy.
Photovoltaic (PV) systems are a strategic approach to exploiting the solar energy.
However, the harvesting energy of the PV module is low conversion efficiency, nonlinear
characteristics and dependent on the temperature and the amount of irradiance. Maximum
power point tracking (MPPT) techniques are a practical solution to maximise the output
of the PV system and overcome nonlinear characteristics under all circumstances. Many
MPPT algorithms have been proposed. Most MPPT algorithms suffer from oscillation
where the operation points oscillate around the maximum power point. As a result, the
loss of power is increased. The loss of tracking under a dynamic change in irradiance is
another challenge in MPPT. In this work, a new MPPT algorithm based on the Parabolic
Prediction Method is proposed to track the maximum power point. The proposed method
can overcome the limitation of conventional MPPT algorithms such as steady state
oscillation and loss of tracking during a dynamic change in irradiance. The working
principle of this method is the calculation of the maximum power from a parabolic convex
function. Subsequently, a methodical scheme is sophisticated to regulate the concavity
and optimum region of the approximate parabola for guaranteeing the repetitive
convergence of the proposed algorithm. To validate its superiority, the proposed method
is compared with the conventional P&O method in terms of steady state and dynamic
change in irradiance conditions. The algorithm is carried out on a DC-DC buck converter.
The verification of the proposed method has been done using MATLAB/Simulink®. The
results prove that the proposed MPPT algorithm tracks the maximum power successfully
within a short time of 100 ms that is less than the conventional P&O method. Besides
that, the proposed method has a faster dynamic response and removes oscillations of the
operating point around the maximum power point (MPP) under steady state conditions.
In a dynamic change in irradiance, the MPPT algorithm needs less than 100msec to reach
the new maximum power compared to the conventional P&O algorithm that needs more
than 100msec. For all case tests, the proposed algorithm has zero oscillation after
reaching the maximum compared to the conventional P&O algorithm that continues in
oscillation even after reaching maximum powe
Optimal race analysis parameters of freestyle swimming events: a case study
No full textLink to publisher's homepage at https://www.mohejournal.org/aboutus.aspIntroduction: During the swim meet, race analysis is a common practice to provide insight into each event. This
case study explores the variables of swimming performance using the video analysis method.
Purpose: To determine the best indicator from a set of swim variables (digitised from video) for competitive swim
races by one Malaysian freestyle swimmer in preparation for the Tokyo Olympic Games 2020.
Methods: Race video footage was analysed retrospectively to determine the key parameter for each event
distance. The following variables were calculated: start time, end time (ET), turn time (TT), stroke count, stroke
length, stroke rate, average velocity (AV) and stroke index. Differences were subsequently assessed among the
parameters within the same event style.
Results: The results from the correlation test between the eight digitised variables and final time (FT) showed
that for both 200 and 400 m events the variables AV (respectively, r = −0.96 and r = −0.94) and TT (respectively,
r = 0.89 and r = 0.83) were significantly correlated. In addition, for the 200 m events, the ET also significantly
correlated (r = −0.94) with FT.
Conclusion: This swimmer and over this period of Olympic qualifiers competitions, AV and TT were the best
indicators for swim performance. Regarding the 200 m events, the end (sprint) time may also be an indicator