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arXiv 2018年

作者： HESSENTHALER, ANDREAS SOUTHWORTH, BEN S. NORDSLETTEN, DAVID RÖHRLE, OLIVER FALGOUT, ROBERT D. SCHRODER, JACOB B. Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Pfaffenwaldring 5a Stuttgart70565 Germany Department of Applied Mathematics University of Colorado at Boulder CO United States Division of Imaging Sciences and Biomedical Engineering King's College London St. Thomas Hospital 4th Floor Lambeth Wing LondonSE1 7EH United Kingdom Center for Applied Scientific Computing Lawrence Livermore National Laboratory P.O. Box 808 L-561 LivermoreCA94551 United States Department of Mathematics and Statistics University of New Mexico 310 SMLC AlbuquerqueNM87131 United States

This paper presents a multilevel convergence framework for multigrid-reduction-intime (MGRIT) as a generalization of previous two-grid estimates. The framework provides a priori upper bounds on the convergence of MGRIT V- and F-cycles, with different relaxation schemes, by deriving the respective residual and error propagation operators. The residual and error operators are functions of the time stepping operator, analyzed directly and bounded in norm, both numerically and analytically. We present various upper bounds of different computational cost and varying sharpness. These upper bounds are complemented by proposing analytic formulae for the approximate convergence factor of V-cycle algorithms that take the number of fine grid time points, the temporal coarsening factors, and the eigenvalues of the time stepping operator as parameters. The paper concludes with supporting numerical investigations of parabolic (anisotropic diffusion) and hyperbolic (wave equation) model problems. We assess the sharpness of the bounds and the quality of the approximate convergence factors. Observations from these numerical investigations demonstrate the value of the proposed multilevel convergence framework for estimating MGRIT convergence a priori and for the design of a convergent algorithm. We further highlight that observations in the literature are captured by the theory, including that two-level Parareal and multilevel MGRIT with F-relaxation do not yield scalable algorithms and the benefit of a stronger relaxation scheme. An important observation is that with increasing numbers of levels MGRIT convergence deteriorates for the hyperbolic model problem, while constant convergence factors can be achieved for the diffusion equation. The theory also indicates that L-stable Runge-Kutta schemes are more amendable to multilevel parallel-in-time integration with MGRIT than A-stable Runge-Kutta schemes. Copyright © 2018, The Authors. All rights reserved.

关键词： Coarsening

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Particle swarm optimization based maximum power point tracking for partially shaded photovoltaic arrays

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International Journal of simulation: Systems, Science and Technology 2016年第34期17卷 20.1-20.7页

作者： Teo, Kenneth Tze Kin Lim, Pei Yi Chua, Bih Lii Goh, Hui Hwang Tan, Min Keng Modelling Simulation & Computing Laboratory Faculty of Engineering Universiti Malaysia Sabah Kota KinabaluSabah Malaysia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia

This paper presents particle swarm optimization based perturb and observe (PSO-P&O) algorithm for maximizing output power of photovoltaic (PV) array under partially shaded conditions (PSC). During PSC, the P-V characteristic of PV will become more complex with multiple maximum power points (MPP). Most of the conventional maximum power point tracking (MPPT) algorithms, such as P&O, will be trapped at the local MPP and hence limiting the maximum power generation. As such, investigation on PSO-P&O algorithm is carried out to maximize the PV generated power principally under PSC operation. The performances of conventional P&O and the proposed PSO-P&O algorithms are investigated particularly on the transient and steady state responses under various shaded conditions. The simulation results show the developed PSO- P&O algorithm is able to facilitate the PV array to reach the global MPP and assist the PV array to produce more stable output power compared to the conventional P&O algorithm. © 2016, UK simulation Society. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Exploration of genetic algorithm in network coding for wireless sensor networks

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International Journal of simulation: Systems, Science and Technology 2014年第6期15卷 83-89页

作者： Teo, Kenneth Tze Kin Chin, Renee Ka Yin Tan, Shee Eng Lee, Chun Hoe Lim, Kit Guan Modelling Simulation and Computing Laboratory Material & Mineral Research Unit Universiti Malaysia Sabah Kota Kinabalu Malaysia

Wireless network comprises of multiples nodes that work together to form a network. Each node in a wireless network communicates with one another by disseminating information packet among them. Source node and destination node are often far apart from each other, thus the information packet has to be transmitted to intermediate node(s) before it is able to be relayed to its destination. Network coding is introduced to combine several packets from different sources and broadcast the combined packet to several destinations in single transmission time slot. Each destination is capable to extract the intended information by decoding from a common packet. In short, network coding improves the throughput for wireless and wired networks but also causes side effects such as complexity of packets management and increases delay for coding opportunity. Hence, genetic algorithm is used to optimize the resources for network coding. Genetic algorithm will search for optimum routes to the destination according to the desired throughput with a desired multicast rate. In this paper, genetic algorithm is further enhanced in searching of optimum route for a packet. The simulation results show the enhanced genetic algorithm can adapt to various situations with different topologies with a better throughput and energy consumption compared to the store-and-forward method used in conventional wireless sensor network. © 2014, UK simulation Society. All rights reserved.

关键词： Network coding

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Evaluation for Voltage Stability Indices in Power System Using Artificial Neural Network

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Procedia Engineering 2015年 118卷 1127-1136页

作者： H.H. Goh Q.S. Chua S.W. Lee B.C. Kok K.C. Goh K.T.K. Teo Department of Electrical Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia 86400 Parit Raja Batu Pahat Johor Malaysia Modelling Simulation and Computing Laboratory Level 3 Block C School of Engineering and Information Technology Universiti Malaysia Sabah Jalan UMS 88400 Kota Kinabalu Sabah Malaysia

At present, the evaluation of voltage stability assessment experiences sizeable anxiety in the safe operation of power systems. This is due to the complications of a strain power system. With the snowballing of power demand by the consumers and also the restricted amount of power sources, therefore, the system has to perform at its maximum proficiency. Consequently, the noteworthy to discover the maximum ability boundary prior to voltage collapse should be undertaken. A preliminary warning can be perceived to evade the interruption of power system's capacity. This paper considered the implementation of real-time system monitoring methods that able to provide a timely warning in the power system before the voltage collapse occurred. Numerous types of line voltage stability indices (LVSI) are differentiated in this paper to resolve their effectuality to determine the weakest lines for the power system. The line voltage stability indices are assessed using the IEEE 9-Bus and IEEE 14-Bus Systems to validate their practicability. Besides that, this paper also introduced the implementation of real-time voltage stability monitoring by using Artificial Neural Network (ANN). Results demonstrated that the calculated indices and the estimated indices by using ANN are practically relevant in predicting the manifestation of voltage collapse in the system. Therefore, essential actions can be taken by the operators in order to dodge voltage collapse incident from arising.

关键词： Artificial Neural Network (ANN) Line Voltage Stability Indices (LVSI) Maximum Loadability Voltage Stability Analysis

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Trajectory Clustering for Behavioral Pattern Learning in Transportation Surveillance

Trajectory Clustering for Behavioral Pattern Learning in Tra...

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Artificial Intelligence with Applications in Engineering and Technology (ICAIET)

作者： Mei Yeen Choong Renee Ka Yin Chin Kiam Beng Yeo Kenneth Tze Kin Teo Modelling Simulation & Computing Laboratory Universiti Malaysia Sabah Kota Kinabalu Malaysia Universiti Malaysia Sabah Kota Kinabalu Sabah MY

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.

关键词： Trajectory Vehicles Surveillance Radar tracking Traffic control Clustering algorithms Roads

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Performance Analysis of Enhanced Genetic Algorithm Based Network Coding in Wireless Networks

Performance Analysis of Enhanced Genetic Algorithm Based Net...

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Asia International Conference on Modeling & simulation

作者： Kenneth Tze Kin Teo Renee Ka Yin Chin Shee Eng Tan Chun Hoe Lee Kit Guan Lim Universiti Malaysia Sabah Kota Kinabalu Sabah MY Modelling Simulation & Computing Laboratory Material & Mineral Research Unit Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia

Network coding combines several packets from different sources and broadcasts the combined packet to several destinations in single transmission time slot. Each destination is capable to extract the intended information by decoding from a common packet. In short, network coding improves the throughput for wireless and wired networks but also causes side effects such as complexity of packets management and increased delay for coding opportunity. Hence, genetic algorithm is used to optimize the resources for network coding. Genetic algorithm will search for suitable routes to the destination according to the desired throughput with a desired multicast rate. In this paper, genetic algorithm is further enhanced in searching of optimum route for a packet. The simulation results show that enhanced genetic algorithm can adapt to various situations with different topologies, providing an optimum route for a packet in the network.

关键词： Genetic algorithms Biological cells Network coding Encoding Sociology Statistics Throughput

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Vehicle Tracking Using Particle Filter for Parking Management System

Vehicle Tracking Using Particle Filter for Parking Managemen...

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Artificial Intelligence with Applications in Engineering and Technology (ICAIET)

作者： Kenneth Tze Kin Teo Renee Ka Yin Chin N.S.V. Kameswara Rao Farrah Wong Wei Leong Khong Universiti Malaysia Sabah Kota Kinabalu Sabah MY Modelling Simulation & Computing Laboratory Material & Mineral Research Unit Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia

Increment of on-road vehicles has urged public venues to provide visitors with a larger area of parking space. As the parking area grew larger for example in a hyper mall, a well-organized parking management system is necessary to assist drivers in locating parking position. Besides, it can also help the management team to monitor vehicle flow in the parking lot. Vehicle tracking plays an important role to the parking management system, as accurate tracking result will lead to a more efficient management system. Among commercially available sensors, video sensor has been commonly deployed in the parking area due to its ability in obtaining a wide range of vehicle information. However, images captured using video sensors are limited under situations where vehicles are undergoing occlusion and maneuvering incidents. This will cause tracking error therefore affecting the performance of the parking management system. Particle filter has been proven as one of the promising techniques to track vehicle under disturbances. Therefore, particle filter is proposed to track vehicle under occlusion and maneuvering incidents in this study. Experimental results show that the particle filter is able to track a target vehicle under different disturbances.

关键词： Vehicles Target tracking Sensors Genetic algorithms Particle filters Color Shape

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modelling and optimisation of stand alone power generation at rural area

Modelling and optimisation of stand alone power generation a...

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2013 IEEE International Conference on Consumer Electronics - China Workshop, ICCE-China 2013

作者： Teo, Kenneth Tze Kin Goh, Hui Hwang Chua, Bih Lii Tang, Sook Kwan Tan, Min Keng Modelling Simulation and Computing Laboratory Material and Mineral Research Unit Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9781479903207

Hydroelectric power is one of the important renewable energy sources. It converts the potential energy of the natural circulating water flows to usable electrical energy by driving a turbine which is connected to a generator. In the rural area of Sabah state in Malaysia, such as Kampung Terian, the existing micro-hydro power plant is incapable to support the villagers' demand during the peak hours. Meanwhile, the tremendous amount of excess energy is wasted by disposing through ballast load during the non-peak hours. Since the discharge coefficient parameter of charging system is dynamically changed in nature, the conventional proportional controller is unable to control the charging process well. Thus, a lead-acid battery storage system is developed to incorporate with the micro-hydro power plant. Fuzzy logic control is proposed in this work to control the amount of current charging for the battery by manipulating the duty cycle of a DC-DC buck converter. © 2013 IEEE.

关键词： Charging (batteries)

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Adaptive phase synchronisation algorithm for collaborative beamforming in wireless sensor networks

Adaptive phase synchronisation algorithm for collaborative b...

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7th Asia International Conference on Mathematical modelling and Computer simulation, AMS 2013

作者： Wong, Chen How Siew, Zhan Wei Chin, Renee Ka Yin Kiring, Aroland Teo, Kenneth Tze Kin Modelling Simulation and Computing Laboratory School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9780769551012

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. © 2013 IEEE.

关键词： Sensor nodes

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