Mohd Zakimi Zakaria, DrThis page provides access to scholarly publication by UniMAP Faculty members and researchershttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/433102024-03-29T08:51:34Z2024-03-29T08:51:34ZA Computational Study on the Magnetic Resonance Coupling Technique for Wireless Power TransferNur Atiqah, ZakariaMuzammil, JusohNur Hafizah, GhazaliM. N., YasinThennarasan, SabapathyMohamed Nasrun, OsmanMohd Zakimi, Zakariahttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/692422021-01-06T00:39:36Z2017-01-01T00:00:00ZA Computational Study on the Magnetic Resonance Coupling Technique for Wireless Power Transfer
Nur Atiqah, Zakaria; Muzammil, Jusoh; Nur Hafizah, Ghazali; M. N., Yasin; Thennarasan, Sabapathy; Mohamed Nasrun, Osman; Mohd Zakimi, Zakaria
Non-radiative wireless power transfer (WPT) system using magnetic resonance coupling (MRC) technique has recently been a topic of discussion among researchers. This technique discussed more scenarios in mid-range field of wireless power transmission reflected to the distance and efficiency. The WPT system efficiency varies when the coupling distance between two coils involved changes. This could lead to a decisive issue of high efficient power transfer. This paper presents case studies on the relationship of operating range with the efficiency of the MRC technique. Demonstrative WPT system operates at two different frequencies are projected in order to verify performance. The resonance frequencies used are less than 100MHz within range of 10cm to 20cm.
Link to publisher's homepage at https://www.matec-conferences.org/
2017-01-01T00:00:00ZEffects of genetic algorithm parameters on multiobjective optimization algorithm applied to system identification problemMohd Zakimi, ZakariaHishamuddin, JamaluddinRobiah, AhmadAbdul Halim, Muhaiminhttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/443092016-12-02T08:27:08Z2011-01-01T00:00:00ZEffects of genetic algorithm parameters on multiobjective optimization algorithm applied to system identification problem
Mohd Zakimi, Zakaria; Hishamuddin, Jamaluddin; Robiah, Ahmad; Abdul Halim, Muhaimin
The growing interest in multiobjective optimization algorithms and system identification resulted in a huge research area. System identification is about developing a mathematical model for representing the system observed. This paper describes the effects of genetic algorithm parameters used in multiobjective optimization algorithm (MOO) that is applied to system identification problem. Two simulated linear systems with known model structure were considered for representing the system identification problem. The performance metrics used in this study are convergence and diversity metric. These metrics show the performance of MOO when GA parameters are varied. The simulation results show the effects of GA parameter on MOO performance. A right combination of GA parameters used in MOO is shown in this study.
Link to publisher's homepage at http://ieeexplore.ieee.org
2011-01-01T00:00:00ZBee algorithm integrated with system identification technique for modelling dynamic systemsMohd Zakimi, ZakariaNurhidayati, Wahidhttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/440472016-11-17T06:45:22Z2016-04-20T00:00:00ZBee algorithm integrated with system identification technique for modelling dynamic systems
Mohd Zakimi, Zakaria; Nurhidayati, Wahid
System identification has been widely used in modelling dynamic system whereby the input-output data from real system are undergo the model structure selection, parameter estimation and model validation procedure. However, the most complicated part in modelling the dynamic system is selecting the model structure to represent the system. In this project, bee algorithm (BA) is integrated with system identification technique to optimize the model structure selection in modelling the dynamic system. This project describes the procedure and investigates the performance and effectiveness of BA based on a few case studies. The result indicates that the proposed algorithm is able to select the model structure of a system successfully. The validation test carried out demonstrates that BA is capable of producing adequate and parsimonious models effectively.
Link to publisher’s homepage at http://www.arpnjournals.org
2016-04-20T00:00:00ZModeling automotive palm oil biodiesel engine using multi-objective optimization differential evolution algorithmMohd Zakimi, ZakariaAzuwir, Mohd NorHishamuddin, JamaluddinRobiah, Ahmadhttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/438552016-11-07T04:09:40Z2014-01-01T00:00:00ZModeling automotive palm oil biodiesel engine using multi-objective optimization differential evolution algorithm
Mohd Zakimi, Zakaria; Azuwir, Mohd Nor; Hishamuddin, Jamaluddin; Robiah, Ahmad
Modeling automotive palm oil biodiesel engine using the propose algorithm called multi-objective optimization differential evolution (MOODE) is carried out in this paper. The performance of the system is analyzed to study the dynamic behaviors of using biodiesel in running the engine. An adequate modeling of the system is needed to design an exclusive engine controller. The biodiesel engine is treated as a black box where the acquired input-output data is used in the modeling processes. Two objective functions are considered for optimization; minimizing the number of term of a model structure and minimizing the mean square error between actual and predicted outputs. Nonlinear auto-regressive with exogenous input (NARX) model is used to represent the mathematical model of the investigated system. To obtain an optimal model for representing the dynamic behavior of automotive palm oil biodiesel engine, the model validity tests have been applied. Therefore, MOODE can be applied for other processes to find an optimal model structure as long as those processes have reliable input output data.
Link to publisher's homepage at http://www.wseas.org
2014-01-01T00:00:00Z