Kenneth Tze Kin Teo

Universiti of Malaysia Sabah

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Min Keng Tan

University of Chinese Academy of Sciences

Kit Guan Lim

Universiti of Malaysia Sabah

Renee Ka Yin Chin

Universiti of Malaysia Sabah

Helen Sin Ee Chuo

Universiti of Malaysia Sabah

Yit Kwong Chin

Universiti Sains Malaysia

Kiam Beng Yeo

Universiti of Malaysia Sabah

Zhan Wei Siew

Universiti of Malaysia Sabah

Bih Lii Chua

Universiti of Malaysia Sabah

Heng Jin Tham

Universiti of Malaysia Sabah

Lorita Angeline

Universiti of Malaysia Sabah

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Universiti of Malaysia Sabah

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Conference Board

Information Technology University

American Society of Safety Professionals

University of Technology Malaysia

Wuyi University

National University of Malaysia

University of Malaya

Tun Hussein Onn University of Malaysia

Universiti Teknologi MARA

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Enhancement of Markov Chain Monte Carlo Convergence Speed in Vehicle Tracking Using Genetic Operator

Wei Yeang Kow Wei Leong Khong Yit Kwong Chin Ismail Saad Kenneth Tze Kin Teo

Markov Chain Monte Carlo (MCMC) has been essential in tracking vehicle undergoing disturbances for traffic surveillance purposes. It is capable of tracking vehicle by estimating the vehicle's position with the sampling of probability distributions. However the accuracy of the position estimation is highly dependent on the sampling efficiency of MCMC. Therefore the sample size of the MCMC is adapted to track the vehicle according to the disturbances encountered. The adaptive sample size of MCMC is determined by using the CUSUM path plot and variance ratio convergence diagnostic algorithm. To further enhance the convergence speed, genetic crossover and mutation operator is introduced into the adaptive MCMC. The genetic operator (GO) is capable of reduces the variance between samples and hence allowing faster convergence speed on the MCMC samples. Experimental results have shown that the GO adaptive MCMC tracking algorithm have better tracking performances with consumption of lesser sample size.

10.1109/cimsim.2012.61

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Adapting Photovoltaic Perturbation Voltage with Particle Swarm Optimization

Min Keng Tan Kah Ming Yap Kit Guan Lim Soo Siang Yang Pungut Ibrahim

This work presents a particle swarm optimization (PSO) based maximum power point tracking (MPPT) system for maximizing the output power of a photovoltaic (PV) system and minimizing the output power fluctuation. Under partially shaded condition (PSC), the P-V characteristics will exhibit multiple peaks. The conventional MPPT approaches, such as perturb and observe (P&O) algorithm will tend to track the local maximum power point (LMPP) instead of the global maximum power point (GMPP). The fix perturbation size of the P&O algorithm will lead to power losses as the output power fluctuates at steady state. Thus, PSO based MPPT algorithm is proposed to track the GMPP and minimize the fluctuation in output power. The proposed algorithm will determine the optimum perturbation size for MPPT controller based on the information of change in voltage and change in power. The simulation results show that the optimized MPPT is able to track the GMPP under PSC accurately, fast and with zero oscillation in comparison with conventional MPPT. Parameter's tuning is important for PSO based MPPT to achieve good transient response, particularly the inertia weight. In conclusion, the PSO based MPPT can ensure optimal and stable power generation from the PV system.

10.1109/scored53546.2021.9652760

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Trajectory Clustering for Behavioral Pattern Learning in Transportation Surveillance

Mei Yeen Choong Renee Ka Yin Chin Kiam Beng Yeo Kenneth Tze Kin Teo

The development of an efficient traffic flow monitoring system has been the main focus for many researchers working in the field. Due to the rapid development in urbanization, the complexity of traffic intersections provides challenges for researchers to detect the underlying traffic scenes. With the emerging video based surveillance system, vehicle trajectory can be extracted for observation and prediction via behavioral pattern learning. Prior to the learning, clustering of the extracted vehicle trajectory data is performed to group the data based on similarity measures. In this paper, the implementation of clustering algorithm on the trajectory data is analyzed and issues concerning the trajectory clustering are discussed.

Similarity (geometry)

Urban Computing

10.1109/icaiet.2014.41

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Adaptive Phase Synchronisation Algorithm for Collaborative Beamforming in Wireless Sensor Networks

Chen How Wong Zhan Wei Siew Renee Ka Yin Chin Aroland Kiring Kenneth Tze Kin Teo

Collaborative beam forming (CB) is a cooperative technique that utilizes a group of distributed wireless nodes to collectively transmit a common message with proper phase weights to an intended direction. As a result, CB significantly extends the communication range of the sensor node in wireless sensor networks (WSNs). Each sensor node in CB has an independent local oscillator. It becomes a vital problem to achieve CB as the distributed nodes are unaware of their phase relationship. Therefore, an efficient phase synchronisation algorithm is needed as the distributed nodes are unaware of their phase relationship. In this paper, an iterative phase synchronisation algorithm using (1+1)-evolution strategy (ES) is proposed to achieve CB in WSNs. Evaluations have been carried out through simulation and the results show that the proposed algorithm is more energy efficient in comparison to the existing phase synchronisation algorithm.

Brooks–Iyengar algorithm

10.1109/ams.2013.50

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Advanced Fault Detection in DC Microgrid System using Reinforcement Learning

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) (2021)

Min Keng Tan Kar Leong Lee Kit Guan Lim Ahmad Razani Haron Pungut Ibrahim

As technologies are expanding, the demand for power supply also increases. This causes the demand for power is difficult to be fulfilled as non-renewable sources are reducing. Therefore, the microgrid concept is introduced, where it is constructed with renewable energy sources, energy storage devices and loads. There are two types of microgrid, which are alternating current (AC) microgrid and direct current (DC) microgrid. Various research show that DC microgrid has more advantages over AC microgrid. However, DC microgrid is not widely used due to the lack of studies on it compared to AC microgrid. Besides, DC microgrid has one significant problem not fixed, which is the fault in the DC microgrid. Whenever a fault occurs, the whole DC microgrid will be affected rapidly. Therefore, this project aims to design a fault detector based on artificial intelligence to detect the fault and isolate the fault effectively. A fault detector based artificial intelligence should be implemented into the DC microgrid system to protect it. Two techniques in Artificial Immune System are being compared. The results showed that the improved Negative Selection Algorithm with variable sized detector has better performance than the general Negative Selection Algorithm with constant sized radius in detecting fault in DC microgrid system.

Microgrid

10.1109/iicaiet51634.2021.9573711

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ZnO thickness and ZnTe back contact effect of CdTe thin film solar cell Voc and efficiency progression

Materials Research Express (2021)

Bablu K. Ghosh Syafiqa Nasir Kenneth Tze Kin Teo Ismail Saad

Abstract CdTe thin film (TF) solar cells are most promising photovoltaic (PV) technology in commercial platform. Back contacts and interface defects related opto-electrical losses are still vital to limit its further technological benefit. TF PV cells shallow recombination and parasitic loss lessening purpose carrier selective back contact with band matching window layers are essential. Beside that back and front contact thickness choice is vital for field associated selective carrier collection and generous optical transmission into the active junction of the cell. It can make variation of cell efficiency. Window and front contact layers band edge variation and back contact thickness effect is analyzed by SCAPS-1D simulation software. ZnO and SnO 2 front contact for CdS and CdSe window layers effect are numerically studied for 1 μ m CdTe thin film PV cell. Significance of materials for front contact and its thickness effect on current density while ZnTe back surface field contact thickness effect on open circuit voltage and efficiency are demonstrated. Finally, ZnO/CdS/CdTe/ZnTe cell of 0.925 V open circuit voltage and 19.06% efficiency has been achieved for 90 nm of ZnTe with Molybdenum (Mo) back contact.

Cadmium telluride photovoltaics

Open-circuit voltage

Electrical contacts

10.1088/2053-1591/ac38de

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The Integrated Inventory Management with Forecast System

International Journal of Advances in Management Science (2014)

Noor Ajian Mohd-Lair Chin-Chong Ng Abdullah Mohd-Tahir Rachel Fran Mansa Kenneth Tze Kin Teo

Inventory Management System is very important for driving a company into better achievement. The main objective of inventory management is to keep the inventory level as low as possible and meet customers' demand. This project centers on development of the computerized integrated inventory management and forecast system for the Guan Lee Sdn Bhd. In this project, the exponential smoothing is selected to predict demands as inputs to control the computerised inventory management system. The integrated system was written using the Visual Basic 2008. This integrated inventory management and forecast system has the ability to forecast while effectively control on inventory level with six specific features of alert, creation, inventory, transfer, search and reports. Performance of the system was analyzed with three types of forecast value (actual and adjusted forecast values). From the results, the actual forecast values tend to move toward the ideal one. Thus, the forecast system is proved to be reliable and accurate. The suggested improvements are auto recording of historical data, attachment of picture to each item, and sending notification through text message.

10.14355/ijams.2014.0301.11

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Maximizing Power Generation in Variable Speed Micro-Hydro with Power Point Tracking

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) (2022)

Min Keng Tan Norafe Maximo Javinez Kit Guan Lim Ahmad Razani Haron Pungut Ibrahim

Conventional variable speed micro-hydro control systems suffer from non-optimal input control. The controllers estimate the changes in flow rate without anticipating the global maximum power curve. As such, this paper aims to explore and develop a feasible maximum power point tracker (MPPT) with perturb and observe (P&O) and genetic algorithm (GA) in providing optimal power generation for variable speed micro-hydro system. This research first introduces a mathematical model for an experimental variable speed micro-hydro platform and then simulates the micro-hydro in MATLAB. Conventional P&O MPPT algorithm used fixed perturbation size which requires large computation time when the perturbation size is small and suffers from power fluctuation issues when the perturbation size is large. Thus, a GA-based P&O MPPT algorithm with adaptive perturbation size is proposed to provide a large perturbation size during transient response and a small perturbation size at a steady state. The simulation results showed that the proposed GA-based P&O MPPT algorithm was able to track the global maximum power point (MPP).

Maximum power principle

10.1109/iicaiet55139.2022.9936859

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Employing Portable Vehicle-To-Infrastructure Transceiver with Network Coding

Kit Guan Lim Zhen Hua Chu Helen Sin Ee Chuo Min Keng Tan Herwansyah bin Lago

In Vehicle-to-Infrastructure (V2I) technology, transmission between vehicles and infrastructures could be a major challenge due to the moving environment of vehicle, as well as the changing distance and speed of vehicle during the communication with infrastructure. This project starts with the review of past and present researches that are related to V2I communication. Prototype of wireless data transceivers are developed to identify the optimum transmission range. Store-and-forward (SF) algorithm is a process of storing and forwarding data from one data transceiver to another data transceiver. This technique is used to overcome the limitation of transmission range by continuously passing information from one point to another. However, during the transmission, the process taken for data exchanging will affect the entire transmission time. Thus, an algorithm named Network coding (NC) is introduced to reduce the transmission while maintaining the stable connection. NC is a process of combining two or more sources of data to reduce number of transmissions, which is very useful in data exchanging. These algorithms are computed in MATLAB and simulated for performance analysis. Apart from that, SF and NC are also programmed into wireless data transceivers to carry out real time experiment. Simulation results showed that SF uses 0.1664 s to exchange 256 bits data while NC spends 0.1266 s to complete the same mission. Hence NC is showing better performance than SF.

Transceiver

Linear network coding

10.1109/scored53546.2021.9652733

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Variational Level Set Algorithm in Image Segmentation for Foetus Ultrasound Imaging System

International Journal of Simulation Systems Science & Technology (2012)

Mei Yeen Choong May Chin Seng Kenneth Tze Kin Teo

Segmentation on ultrasound image is difficult when the image is not clear and contains unwanted noise. Since the object to be segmented out can be changing in shape for a period of time, there is a need to apply a computerised segmentation method for future analysis without any assumptions about the objectâ€™s topology is made. In general, when performing pregnancy ultrasound scanning, seeking a snapshot with best position or angle of the foetus is often a task done by obstetrician. This snapshot is useful for the obstetrician to locate the crown and the rump of the foetus for specific measurement. In this paper, a computerized segmentation using variational level set algorithm (VLSA) is proposed here. Results showed the variational level set contour evolved well on the low contrast and noise consisting ultrasound image.

Level set method

Ultrasound imaging

10.5013/ijssst.a.13.3b.03

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Citations (3)