Sie Long Kek

Tun Hussein Onn University of Malaysia

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Kok Lay Teo

Sunway University

Sy Yi Sim

Tun Hussein Onn University of Malaysia

Kim Gaik Tay

Tun Hussein Onn University of Malaysia

Rosmila Abdul-Kahar

Tun Hussein Onn University of Malaysia

Mohd Ismail Abd Aziz

University of Technology Malaysia

Wah June Leong

Universiti Putra Malaysia

Mohd Asrul Affendi Abdullah

Tun Hussein Onn University of Malaysia

Cheong Tau Han

Universiti Teknologi MARA

Siti Afiqah Muhamad Jamil

Universiti Teknologi MARA System

Alvin John Lim Meng Siang

Tun Hussein Onn University of Malaysia

Cooperative Institutions

Tun Hussein Onn University of Malaysia

University of Technology Malaysia

Universiti Putra Malaysia

Universiti Teknologi MARA

Universiti Malaysia Sarawak

Yes Technologies (United States)

University of Malaya

Universitatea Națională de Știință și Tehnologie Politehnica București

University of Bucharest

Northern University of Malaysia

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The Richardson's extrapolation graphical user interface excel spreadsheet solver

Journal of Telecommunication Electronic and Computer Engineering (JTEC) (2016)

Kim Gaik Tay Cheong Tau Han Sie Long Kek Rosmila Abdul-Kahar

In this paper, the fourth version of Richardson’s extrapolation Excel spreadsheet calculator for computing differentiations numerically has been upgraded. In this version, a graphical user interface (GUI) form was developed to capture users’ inputs so that users will not confuse the input and output in Excel spreadsheet. It makes this version of GUI Excel spreadsheet solver more user friendly, attractive and interactive. A summative evaluation of this Richardson’s extrapolation GUI Excel spreadsheet solver has been conducted by involving 20 postgraduate students by using questionnaire. The findings showed that the majority of the students agreed that the Richardson’s extrapolation graphical user interface (GUI) spreadsheet solver provides an interesting and interactive learning environment.

Graphical user interface

Solver

Calculator

Interface (matter)

Richardson extrapolation

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Citations (1)

Testing of linear models for optimal control of second-order dynamical system based on model-reality differences

Results in Control and Optimization (2021)

Sie Long Kek Sy Yi Sim Chuei Yee Chen

In this paper, the testing of linear models with different parameter values is conducted for solving the optimal control problem of a second-order dynamical system. The purpose of this testing is to provide the solution with the same structure but different parameter values in the model used. For doing so, the adjusted parameters are added to each model in order to measure the differences between the model used and the plant dynamics. On this basis, an expanded optimal control problem, which combines system optimization and parameter estimation, is introduced. Then, the Hamiltonian function is defined and a set of the necessary conditions is derived. Consequently, a modified model-based optimal control problem has resulted. Follow from this, an equivalent optimization problem without constraints is formulated. During the calculation procedure, the conjugate gradient algorithm is employed to solve the optimization problem, in turn, to update the adjusted parameters repeatedly for obtaining the optimal solution of the model used. Within a given tolerance, the iterative solution of the model used approximates the correct optimal solution of the original linear optimal control problem despite model-reality differences. The results obtained show the applicability of models with the same structures and different parameter values for solving the original linear optimal control problem. In conclusion, the efficiency of the approach proposed is highly verified.

Basis (linear algebra)

10.1016/j.rico.2021.100022

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Fuzzy Inference System Model from Non-Fuzzy Clustering Output

Journal of Engineering and Applied Sciences (2019)

Nur Atiqah Hamzah Sie Long Kek

Fuzzy Inference System (FIS) is a process of mapping input into the desired output using fuzzy logic theory where decisions can be made or pattern are dscerned. Ths study aims to discuss on how non-fuzzy clustering output can be used to constmct a model of FIS. Here, the proposed idea is to show the efficient use of the FIS as a predction model for the data classification. In this study, employment income, self-employment income, property and tramfer received are taken into account for clustering the household income data. Then, the FIS prediction model is built using the center values of clusters formed and the output of FIS is compared to the original cluster in whch the best fit predction model to the data is determined. In conclusion, the best predction model in identify~ngin come class is dscovered based on the Root Mean Square Error (RMSE) value computed.

Fuzzy inference

Fuzzy inference system

10.36478/jeasci.2019.4035.4042

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Enhance Cascaded H-Bridge Multilevel Inverter with Artificial Intelligence Control

Indonesian Journal of Electrical Engineering and Computer Science (2018)

S.Y. Sim C.K. Chia Wahyu Mulyo Utomo Hui Hwang Goh Yonis M. Buswig

This paper proposed a 7-level Cascaded H-Bridge Multilevel Inverter (CHBMI) with two diffenrent controller, ie, PID and Artificial Neural Network (ANN) controller to improve the output voltage performance and achieve a lower Total Harmonic Distortion (THD). A PWM generator is connected to the 7-level CHBMI to provide switching of the MOSFET. The reference signal waveform for the PWM generator is set to be sinusoidal to obtain an ideal AC output voltage waveform from the CHBMI. By tuning the PID controller as well as the self-learning abilities of the ANN controller, switching signals towards the CHBMI can be improved. Simulation results from the general CHBMI together with the proposed PID and ANN controller based 7-level CHBMI models will be compared and discussed to verifyl the proposed ANN controller based 7-level CHBMI achieved a lower output voltage THD value with a better sinusoidal output performance.

H bridge

10.11591/ijeecs.v11.i1.pp105-112

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Citations (7)

Least Squares Solution for Discrete Time Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences

Applied Mathematics (2017)

Sie Long Kek Jiao Li Kok Lay Teo

In this paper, an efficient computational approach is proposed to solve the discrete time nonlinear stochastic optimal control problem. For this purpose, a linear quadratic regulator model, which is a linear dynamical system with the quadratic criterion cost function, is employed. In our approach, the model-based optimal control problem is reformulated into the input-output equations. In this way, the Hankel matrix and the observability matrix are constructed. Further, the sum squares of output error is defined. In these point of views, the least squares optimization problem is introduced, so as the differences between the real output and the model output could be calculated. Applying the first-order derivative to the sum squares of output error, the necessary condition is then derived. After some algebraic manipulations, the optimal control law is produced. By substituting this control policy into the input-output equations, the model output is updated iteratively. For illustration, an example of the direct current and alternating current converter problem is studied. As a result, the model output trajectory of the least squares solution is close to the real output with the smallest sum squares of output error. In conclusion, the efficiency and the accuracy of the approach proposed are highly presented.

Observability

Least-squares function approximation

Matrix (chemical analysis)

10.4236/am.2017.81001

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Citations (5)

New Richardson’s extrapolation spreadsheet calculator using VBA programming for numerical differentiations

AIP conference proceedings (2015)

Kim Gaik Tay Sie Long Kek Rosmila Abdul-Kahar

In this paper, we have further improved the limitations of our previous two Richardson’s extrapolation spreadsheet calculators for computing differentiations numerically. The new feature in this new Richardson’s extrapolation spreadsheet calculator is fully automated up to any level based on the stopping criteria using VBA programming. The new version is more flexible because it is controlled by programming. Furthermore, it reduces computational time and CPU memory.

Calculator

Richardson extrapolation

10.1063/1.4915696

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Citations (3)

Analysing count data of Butterflies communities in Jasin, Melaka: A Poisson regression analysis

Journal of Physics Conference Series (2017)

Siti Afiqah Muhamad Jamil Mohd Asrul Affendi Abdullah Sie Long Kek Maria Elena Nor Maryati Mohamed

Counting outcomes normally have remaining values highly skewed toward the right as they are often characterized by large values of zeros. The data of butterfly communities, had been taken from Jasin, Melaka and consists of 131 number of subject visits in Jasin, Melaka. In this paper, considering the count data of butterfly communities, an analysis is considered Poisson regression analysis as it is assumed to be an alternative way on better suited to the counting process. This research paper is about analysing count data from zero observation ecological inference of butterfly communities in Jasin, Melaka by using Poisson regression analysis. The software for Poisson regression is readily available and it is becoming more widely used in many field of research and the data was analysed by using SAS software. The purpose of analysis comprised the framework of identifying the concerns. Besides, by using Poisson regression analysis, the study determines the fitness of data for accessing the reliability on using the count data. The finding indicates that the highest and lowest number of subject comes from the third family (Nymphalidae) family and fifth (Hesperidae) family and the Poisson distribution seems to fit the zero values.

Overdispersion

10.1088/1742-6596/890/1/012171

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Citations (1)

Desert Sheep in Kordofan Area - Sudan: Production and Marketing

Journal of Science and Technology (2010)

Stephanie See Weng Su Sie Long Kek Mohd Asrul Affendi Abdullah

In this paper, the Black-Litterman model which is the improved mean-variance optimization model, is discussed. Basically, the views given by the investors were incorporated into this model so that their views on risk and return, and risk tolerance could be quantified. For doing so, the market rates of return for the assets were calculated from the geometric mean. Moreover, the views of the investors were expressed in the matrix form. Then, the covariance matrix and the diagonal covariance matrix of the assets return were calculated. Accordingly, the mean rate of the asset return was computed. On this basis, the Black-Litterman optimization model was constructed. This model formulation was done by taking a set of possible rates of return for the assets. Particularly, the corresponding optimal portfolios of the assets with lower risk and higher expected return were further determined. For illustration, the historical return data for S&P 500, 3-month Treasury bill, and 10-year Treasury bond from 1928 to 2016 were employed to demonstrate the formulation of the ideal investment portfolio model. As a result, the efficient frontier of the portfolio is shown and the discussion is made. In conclusion, the Black-Litterman model could provide the optimal investment decision practically.

Black–Litterman model

Rate of return

Efficient frontier

Expected return

Risk–return spectrum

Investment

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Citations (3)

Workforce size problem in manufacturing with dynamic programming approach

AIP conference proceedings (2020)

Mei Kuan So Sie Long Kek

Workforce size problem is a common issue in manufacturing, which is to determine the optimal number of workers required such that the productivity of a product is ensured. As such, the situation of understaffing and overstaffing in the manufacturing could be handled properly, in turn, to save the labour cost and to fulfil the economic supply and demand. In this paper, the workforce size problem in manufacturing is described, and the dynamic programming approach is applied to solve the workforce size problem. In the discussion, the return function of the workforce size problem and the state transition equation are identified. Then, the backward recursion equation of dynamic programming is formulated. According to each stage considered, the possible states are determined, and the related value of the return functions are calculated. It is followed by establishing the optimal decision policy for having the optimal number of workers. For illustration, an example of the workforce size problem in manufacturing, which is on a weekly basis, is studied. The result obtained showed that the minimum recruitment cost is RM33, where the optimal number of workers for the respective weeks is four workers, eight workers, eight workers, six workers, and six workers. In conclusion, the efficiency of the dynamic programming method in solving the workforce size problem in manufacturing is highly demonstrated.

Workforce Planning

Manufacturing

10.1063/5.0018444

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Citations (1)

Conjugate Gradient Approach for Linear Optimal Control of Damped Harmonic Oscillator

Proceedings of the International Conference of Control, Dynamic systems, and Robotics (2019)

Sie Long Kek Wah June Leong Sy Yi Sim Chuei Yee Chan

In this paper, applying the conjugate gradient method to solve the linear optimal control problem is discussed.In the optimization theory, the conjugate gradient method is an efficient computational approach for solving the unconstrained optimization problem, specifically, for quadratic case.Since the linear optimal control problem consists of the quadratic cost function and the linear dynamical system, the practical application of the conjugate gradient method to this kind of problem would be addressed.In our study, the necessary conditions for optimality for the linear optimal control problem are highlighted.Then, the equivalent optimization problem is formulated and the gradient function, which is given by the stationary condition, is evaluated.On this basis, the search direction, which satisfies the conjugacy, is determined definitely.During the iterative procedure, the control sequence is calculated such that the state sequence could be obtained.Once the convergence is achieved, the optimal solution of the linear optimal control problem is obtained.For illustration, the optimal control of damped harmonic oscillator is discussed.The results obtained show the efficiency of the approach used.In conclusion, the application of the conjugate gradient method to linear optimal control problem of the damped harmonic oscillator is highly presented.

Conjugate

Harmonic

Harmonic Analysis

10.11159/cdsr19.130

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