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2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011

作者： Chia, Y.S. Kow, W.Y. Khong, W.L. Kiring, A. Teo, K.T.K. Modelling Simulation and Computing Laboratory School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9781457721502

One of the critical tasks in object tracking is the tracking of fast-moving object in random motion, especially in the field of machine vision applications. An approach towards the hybrid of particle filter (PF) and mean shift (MS) algorithm in visual tracking is proposed. In this proposed system, complete occlusion and random movement of object can be handled due to its ability in predicting the object location with adaptive motion model. In addition, the PF is capable to maintain multiple hypotheses to handle clutters in background and temporary failure. However PF requires a large number of particles to approximate the true posterior of the target dynamics. Therefore, MS algorithm is applied to the sampling process of the PF to move these particles in gradient ascent direction. Consequently a small sample size will be sufficient to represent the system dynamics accurately. The proposed approach is aimed to track the moving object in random directions under varying conditions with acceptable computational time. © 2011 IEEE.

关键词： Bandpass filters

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Genetic algorithm based PID optimization in batch process control

Genetic algorithm based PID optimization in batch process co...

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2011 IEEE Conference on Computer Applications and Industrial Electronics, ICCAIE 2011

作者： Tan, M.K. Chin, Y.K. Tham, H.J. Teo, K.T.K. Modelling Simulation and Computing Laboratory School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9781457720581

The primary aim in batch process is to enhance the process operation in order to achieve high quality and purity product while minimising the production of undesired by-product. However, due to the difficulties to perform online measurement, batch process supervision is based on the direct measurable quantities, such as temperature. During the process, a large amount of exothermic heat is released when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently the quality and purity of the final product will be affected. Therefore, it is important to have a control scheme which is able to balance the needs of process safety with the product quality and purity. Since the chemical industries are still applying PI and PID to control the batch process, researchers are keen to optimize PID parameters using artificial intelligence (AI) techniques. However, most of these PID optimization techniques need online process model to predetermine the optimizer parameters. However in practice, the dynamic model of the batch process is poorly known. As a result, majority of the studies focused on acceptable performance instead of optimum performance of the batch process control. This paper proposes a new genetic algorithm (GA) optimizer which consists of additional information of the online estimated model parameters in addition to the PID parameters as the string of the GA. The simulation results show that the proposed GA auto-tuning method is a better candidate than the regular GA where the estimated model parameters in fitness function is capable to control the process temperature while avoiding model mismatch and disturbance condition. © 2011 IEEE.

关键词： Genetic algorithms

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Q-learning based traffic optimization in management of signal timing plan

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International Journal of simulation: Systems, Science and Technology 2011年第3期12卷 29-35页

作者： Chin, Yit Kwong Bolong, Nurmin Kiring, Aroland Yang, Soo Siang Teo, Kenneth Tze Kin Modelling Simulation and Computing Laboratory School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu Malaysia

Occurrences of traffic congestions within the urban traffic network are increasing in a rapid rate due to the rising traffic demands of the outnumbered vehicles on road. The effectiveness of management from traffic signal timing planner is the key solution to solve the traffic congestions, but unfortunately the current traffic light signal system is not fully optimized based on the dynamic traffic conditions on the road. Adaptable traffic signal timing plan system with ability to learn from their past experiences is needed to overcome the dynamic changes of the urban traffic network. The ability of Q-learning to prospect gains from future actions and obtain rewards from its past experiences allows Q-learning to improve its decisions for the best possible actions. A good valuable performance has been shown by the proposed learning algorithm that able to improve the traffic signal timing plan for the dynamic traffic flows within a traffic network.

关键词： Learning algorithms

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Adapting MCMC with CUSUM path plot for overlapped vehicle tracking

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International Journal of simulation: Systems, Science and Technology 2011年第3期12卷 36-43页

作者： Kow, Wei Yeang Khong, Wei Leong Yew, Hoe Tung Saad, Ismail Teo, Kenneth Tze Kin Modelling Simulation and Computing Laboratory School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu Malaysia

Traffic surveillance using video sensors has been essential over the recent years and it is capable of obtaining wide range of vehicle information. However tracking overlapped vehicles still remain as a challenging task due to the involvement of high dimensional calculation. Conventional fixed sample size Markov Chain Monte Carlo (MCMC) will encounter tracking error if the sample size is insufficient and will be computationally expensive if the sample size is too large. Therefore cumulative sum (CUSUM) path plot is introduced to aid the difficulties in determining the sample size of MCMC. The adaptive sample size of MCMC has shown significant tracking accuracy especially when the vehicle is overlapped. Furthermore, implementation of observation likelihood by fusing colour and edge distance has further enhanced the tracking performances. Experimental result shows that CUSUM path plot algorithm has overcome the limitation of fixed sample size MCMC with better tracking accuracy and lesser computational time.

关键词： Vehicles

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Overlapped vehicle tracking via enhancement of particle filter with adaptive resampling algorithm

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International Journal of simulation: Systems, Science and Technology 2011年第3期12卷 44-51页

作者： Khong, Wei Leong Kow, Wei Yeang Angeline, Lorita Saad, Ismail Teo, Kenneth Tze Kin Modelling Simulation and Computing Laboratory School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu Malaysia

Traffic surveillance and on-road security have elevated the demand of machine vision aided traffic control system. Through the modern video camera technology, vehicle tracking has become a vital approach to assist the on-road traffic systems. In the past, many tracking methods have been developed based on the detail and information extracted from the captured vehicle. However, conventional tracking system need to be improved since the background noises and sudden appear objects will increase the difficulties of continuously tracking the target vehicle. Hence, a particle filter algorithm with adaptive resampling approach has been proposed to overcome the vehicle occlusion problems. In addition, the proposed resampling approach can also be used to solve the common particle degeneracy problem. Experimental results show that the enhanced particle filter equipped with adaptive resampling algorithm is significantly improving the accuracy of the tracking process without compromising the processing time.

关键词： Video cameras

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Effect of partially shaded conditions on photovoltaic array's maximum power point tracking

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International Journal of simulation: Systems, Science and Technology 2011年第3期12卷 52-59页

作者： Chin, Chia Seet Neelakantan, Prabhakaran Yang, Soo Siang Chua, Bih Lii Teo, Kenneth Tze Kin Modelling Simulation and Computing Laboratory School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu Malaysia

Maximum power point tracking algorithm is widely implemented in photovoltaic system to maximize the PV array output power. In general, Perturb and Observe (P&O) is simple thus being selected to continuously track the array maximum power point (MPP). Under uniform solar irradiance, PV array characteristic is non-linear and consisting only one MPP along the functional operating voltage. However, when the PV array is partially shaded, the P-V characteristic becomes more complex with multiple MPPs. The occurrence of multiple MPP might cause the PV array to be trapped at the local MPP. At this operating condition of local MPP, PV array will generate lesser output power. In this study, the performance of PV array is explored especially when each PV module is at 30% and 70% shaded conditions. simulation results show that PV array at absolute MPP can generate greater output power with the largest increased by 74.6% hence achieving higher power efficiency.

关键词： Solar panels

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Optimization of partially shaded PV array using fuzzy MPPT

Optimization of partially shaded PV array using fuzzy MPPT

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IEEE Colloquium on Humanities, Science and Engineering (CHUSER)

作者： C.S. Chin M.K. Tan P. Neelakantan B.L. Chua K.T.K. Teo Modelling Simulation and Computing Laboratory School of Engineering and Information Technology Universiti Malaysia Sabah Malaysia

Solar photovoltaic (PV) array shows non-linear P-V characteristic when the array is illuminated under uniform solar irradiance. In this circumstance, the P-V characteristic consists only one maximum power point (MPP) along the functional operating voltage. However, the P-V characteristic has changed and become more complex with multiple MPP when the array is exposed under partially shaded conditions. This circumstance has caused maximum power point tracking (MPPT) algorithm faces difficulty to allocate the exact optimal operating voltage for PV array to generate maximum power. In this paper, initial voltage tracking function (IVTF) has been introduced to assign new initial voltage for tracking the absolute MPP when the PV array is at shaded conditions. Nevertheless, MPPT approach deals with the challenge of tracking the MPP in the faster mode while maintaining the power stability of the PV array toward the MPP. Therefore, fuzzy logic is embedded in the conventional MPPT to adapt the size of the perturbed voltage. The obtained results show that fuzzy logic based MPPT with IVTF is able to track the absolute MPP faster while minimizing power lost due to the power fluctuations.

关键词： Arrays Fuzzy logic Photovoltaic cells Power generation simulation Semiconductor diodes Solar energy

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Energy efficient clustering algorithm in wireless sensor networks using fuzzy logic control

Energy efficient clustering algorithm in wireless sensor net...

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IEEE Colloquium on Humanities, Science and Engineering (CHUSER)

作者： Z.W. Siew A. Kiring H.T. Yew P. Neelakantan K.T.K. Teo Modelling Simulation and Computing Laboratory School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu Malaysia

In general, environment monitoring cluster based hierarchical routing protocol is among the most common protocol being opted due to the load balancing among each other sensor. Sensors are randomly deployed in a specific area to collect useful information periodically for a few months or even a few years. Therefore, battery power limitation becomes a challenging issue. It is also impractical to maintain the network lifetime by changing the battery frequently. Low energy adaptive cluster hierarchical (LEACH) is one of the common clustering protocols that will elect the cluster head based on the probability model which will possibly lead to a reduce in network lifetime due to election of cluster head with a least desired location in the network. For wireless sensor networks, the distribution of cluster head selection directly influences the network's lifetime. This paper presents factors which will affect the network lifetime and apply fuzzy logic based cluster head selection conducted in base station. The base station considers two selection criteria from sensor nodes which are energy level and distance to the base station to select the suitable cluster head that will prolong the first node die (FND) time, data stream guaranteed for every round and also increase the throughput received by the base station before FND.

关键词： Base stations Protocols Peer to peer computing Mathematical model Clustering algorithms Fuzzy logic Wireless sensor networks

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Genetic algorithm based PID optimization in batch process control

Genetic algorithm based PID optimization in batch process co...

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International Conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： M.K. Tan Y.K. Chin H.J. Tham K.T.K. Teo Modelling Simulation and Computing Laboratory School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu Malaysia

关键词： Inductors Heating Batch production systems Genetic algorithms Coolants Optimization Process control

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Adaptive hybrid channel assignment in wireless mobile network via genetic algorithm

Adaptive hybrid channel assignment in wireless mobile networ...

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International Conference on Hybrid Intelligent Systems (HIS)

作者： Y. S. Chia Z. W. Siew A. Kiring S. S. Yang K. T. K. Teo Modelling Simulation and Computing Laboratory School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu Malaysia

In wireless mobile network, the challenge is to assign appropriate frequency spectrum channels to requested calls while maintaining a desirable level of electromagnetic compatibility (EMC) constraint. An effective channel assignment technique is important to improve the system capacity and to reduce the effect of the interference. Most of the existing channel assignment approaches are based on deterministic methods. In this paper, an adaptive hybrid channel assignment (HCA) technique based on genetic algorithm (GA) is proposed. The proposed GA based optimization in HCA scheme is capable to adapt the population size to the number of eligible channels for a particular cell upon new call arrivals in order to achieve faster convergence speed. Besides, the proposed approach can handle both the reassignment of existing calls as well as the allocation of channel to a new call in an adaptive process to maximize the utility of the limited frequency spectrum. The simulation for both uniform and nonuniform traffic distributions on a 49-cells network model show that the average new incoming call blocking or call dropping probability for the proposed hybrid channel optimization method is lower than the deterministic HCA methods.

关键词： Genetic algorithms Vectors Interference Channel allocation Resource management Biological cells Optimization

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