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6th IEEE International Conference on Engineering, Technologies and Applied Sciences, ICETAS 2019

作者： Tan, Min Keng Ee Chuo, Helen Sin Yeo, Kiam Beng Chin, Renee Ka Yin Huang, Sha Teo, Kenneth Tze Kin Modelling Simulation Computing Laboratory Artificial Intelligence Research Unit Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia Faculty of Medicine and Health Sciences Universiti Malaysia Sabah Kota Kinabalu Malaysia School of Rail Transportation Wuyi University Jiangmen China

ISBN: (纸本)9781728140827

This work aims to explore the potential to minimize traffic congestion using a non-deterministic algorithm. Conventionally, the deterministic algorithm such as fuzzy logic was proposed as the computational algorithm to compute the optimum traffic signal timing for minimizing vehicles in queue and travel delay. However, it is very difficult to define the suitable number of fuzzy rules that are able to cover the all possibilities of traffic flow changes since the natural traffic flow behavior is dynamic. Besides, the inherent deterministic behavior limits the algorithm to explore the solution space in searching for the optimum traffic solution. In other words, the deterministic algorithm will not provide other solution with the same input. Therefore, genetic algorithm, a non-deterministic algorithm, is proposed to optimize the traffic signalization. A benchmarked 3×3 grid traffic network is developed as the testbed to examine the robustness of the proposed GA. Each intersection is integrated with a GA based signal controller or known as agent to form a multi-agent system. Each agent has the autonomy in controlling their own traffic intersection and they will share their local traffic information to their downstream intersections. The performance of the proposed GA is compared with the conventional fuzzy logic. The simulation results show the proposed GA improves the performance about 6.6 % in minimizing vehicles in queue and travel delay as compared to the conventional fuzzy logic. © 2019 IEEE.

关键词： Genetic algorithms

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Enhancement of ant colony optimization in multi-robot source seeking coordination 7

Enhancement of ant colony optimization in multi-robot source...

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7th IEEE Conference on Systems, Process and Control, ICSPC 2019

作者： Lee, Jun Wei Tang, Nyiak Tien Lim, Kit Guan Tan, Min Keng Yang, Baojian Teo, Kenneth Tze Kin Modelling Simulation and Computing Laboratory Artificial Intelligence Research Unit Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia Deparment of Intelligent Manufacturing Wuyi University Jiangmen China

ISBN: (纸本)9781728118260

This research presents dynamic approaches for swarm robotics system and subsequently achieve enhanced strategies to enhance equilibrium and optimize power usage. Method apply in progress of the project can be divided into hardware platform, control and optimization, and lastly measurement and analysis method. In hardware platform, the speed of rotation of the wheel is controlled for various movement such as direct motion and rotation in place. Optimization method is focused on ant colony optimization. The corrected equation for robot localization control provides more precise mathematical model for manipulating the robot motion. This research compared ACO, dynamic ACO and Dijkstra algorithm in simulated static condition. The result shows that Standard ACO outperforms others algorithm in static condition while Improved algorithm is best used in dynamic conditions. © 2019 IEEE.

关键词： Ant colony optimization

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Metaheuristic Multi-Hop Clustering Optimization for Energy-Efficient Wireless Sensor Network

Metaheuristic Multi-Hop Clustering Optimization for Energy-E...

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

作者： Vincent Chung Norah Tuah Kit Guan Lim Min Keng Tan Ismail Saad Kenneth Tze Kin Teo Faculty of Engineering Modelling Simulation & Computing Laboratory Universiti Malaysia Sabah Kota Kinabalu Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Kota Kinabalu Malaysia Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (数字)9781728169460

ISBN: (纸本)9781728169606

Energy-efficient optimization algorithm in wireless sensor network (WSN) is often based on solely cluster routing or multi-hop routing. The cluster optimization algorithm will form a cluster network by dividing the sensor nodes into few clusters where each cluster has a cluster head (CH) for data collection. On the other hand, multi-hop optimization algorithm will form a multi-hop network by transmitting data to base station (BS) through data multi-hopping between sensor nodes. However, cluster optimization algorithm suffers from the overburdens of CH nodes, while multi-hop optimization algorithm suffers from the overburdens of nodes which are near to the BS. Therefore, Genetic Algorithm-Cuckoo Search (GACS) is proposed and developed based on the multi-hop clustering model in this paper. GACS optimizes both intra-cluster and inter-cluster communications to enhance energy efficiency in WSN, extending the network lifetime. Based on the performance evaluation, GACS outperforms both Genetic Algorithm (GA)-based cluster optimization algorithm and Cuckoo Search (CS)-based multi-hop optimization algorithm.

关键词： Optimization Clustering algorithms Wireless sensor networks Computational modeling Spread spectrum communication Routing protocols Routing

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Genetic Algorithm based Chain Leader Election in Wireless Sensor Network for Precision Farming

Genetic Algorithm based Chain Leader Election in Wireless Se...

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

作者： Hamzarul Alif Hamzah Norah Tuah Kit Guan Lim Min Keng Tan Ismail Saad Kenneth Tze Kin Teo Faculty of Engineering Modelling Simulation & Computing Laboratory Universiti Malaysia Sabah Kota Kinabalu Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Kota Kinabalu Malaysia Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (数字)9781728169460

ISBN: (纸本)9781728169606

Wireless Sensor Network (WSN) is one of the commonly used technologies in Precision Farming (PF). It provides farmers with accurate real-time information on their farms. In practice, WSN consists of numerous wireless sensor nodes, where each node relies on limited energy sources such as battery to maintain its operation. The energy management issue in WSN has gained attention of scholars, leading to new protocols or schemes developed over the years. Conventionally, PEGASIS protocol selects chain leader without considering the distance and residual energy level of each sensor node. As such, it might increase the energy consumption rate to transmit collected data from sensor node to sink. Inadequate energy management leads to rapid energy drain and eventually shorten the lifespan of WSN. Therefore, this study aims to prolong the lifespan of WSN while minimizing the energy consumption. Genetic Algorithm (GA) is proposed to enhance the chain leader selection scheme of the conventional PEGASIS. The proposed GA will select optimum chain leader by considering the energy consumption rate of each node. As such, the proposed algorithm is able to increase node's transmission as well as improve the lifespan of WSN by 50% as compared to the conventional approach.

关键词： Wireless sensor networks Protocols Peer-to-peer computing Mathematical model Data communication Genetic algorithms Energy consumption

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Segmenting nodules of lung tomography image with level set algorithm and neural network 7

Segmenting nodules of lung tomography image with level set a...

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7th IEEE Conference on Systems, Process and Control, ICSPC 2019

作者： Chong, Soon Yee Tan, Min Keng Yeo, Kiam Beng Ibrahim, Mohd Yusof Hao, Xiaoxi Teo, Kenneth Tze Kin Modelling Simulation Computing Laboratory Artificial Intelligence Research Unit Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia Faculty of Medicine and Health Sciences Universiti Malaysia Sabah Kota Kinabalu Malaysia Wuyi University Department of Intelligent Manufacturing Jiangmen China

ISBN: (纸本)9781728118260

Lung cancer is the main cause of death in the world. It is diagnosed generally by analysing a tissue cluster formation called 'nodule' inside the lung. Computer aided diagnosis (CAD) plays an important role in medical field which helps radiologists to detect and localise lung nodule. The aim of this research is to develop an image segmentation algorithm for nodule detection in computed tomography (CT) image. Performance of the developed segmentation algorithm is analysed through testing on the lung CT image obtained from public online database, Lung Image Database Consortium (LIDC). To segment the lung nodule, preprocessing techniques are applied on the CT image followed by segmentation approaches to segment the nodule inside the lung. In this research, median filter is applied to improve the quality and filter the background noise of the image. Segmentation technique which is level set method (LSM) is applied to segment the nodule. After that, feature extraction process is carried out to obtain the essential information of the segmented nodules for further analysis or classification purpose. Here, the features extracted including centroid, major and minor axis length and area of the nodule. Performance of the segmentation algorithms is analysed using 20 lung CT images from LIDC dataset. Lastly, classification step is done using Artificial Neural Network (ANN) to classify the segmented nodule to different categories. From the analysis, LSM technique performs better in cases of bigger nodule compared to smaller size nodule. The result shows that the proposed segmentation method can effectively segment the lung nodule. © 2019 IEEE.

关键词： Neural networks

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Data transmission in wireless sensor network with greedy function and particle swarm optimization 7

Data transmission in wireless sensor network with greedy fun...

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7th IEEE Conference on Systems, Process and Control, ICSPC 2019

作者： Hamzah, Hamzarul Alif Tuah, Norah Lim, Kit Guan Tan, Min Keng Zhu, Lei Teo, Kenneth Tze Kin Modelling Simulation and Computing Laboratory Artificial Intelligence Research Unit Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Kota Kinabalu Malaysia Communication and Electronics Engineering Institute Qiqihar University Qiqihar China

ISBN: (数字)9781728118260

ISBN: (纸本)9781728118260

Precision farming is a farming management concept based on observing, measuring and responding to the varying crops' needs. For this research, Wireless Sensor Network (WSN) is proposed to be implemented in precision farming to act as a decision support system for the farmers. However, WSN conventional system namely Direct Transmission (DT) protocol suffers high energy consumption, reducing the monitoring ability of the farmers due to the fast energy-depleting sensor nodes. Acknowledging this problem, researchers had developed many protocols such as Quality of Service (QoS), Low Energy Adaptive Clustering Hierarchy (LEACH), Location-based, and Power-Efficient Gathering in Sensor Information System (PEGASIS). For this research, PEGASIS is selected for its high energy-efficiency and compatibility with the proposed system. As distances affect greatly on the energy consumption, Particle Swarm Optimization (PSO) is developed to replace greedy algorithm in PEGASIS to reduce the distances of data transmission. From the tests, PSO is able to reduce the total chain distance (TCD) by up to 7.69% in comparison to the greedy algorithm. © 2019 IEEE.

关键词： Decision support systems

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Improvement of Particle Swarm Optimization in Multi-Robot Trajectory Motion Coordination

Improvement of Particle Swarm Optimization in Multi-Robot Tr...

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IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS)

作者： Nyiak Tien Tang Jun Wei Lee Kit Guan Lim Min Keng Tan Tianlei Wang Kenneth Tze Kin Teo Modelling Simulation & Computing Laboratory |Artificial Intelligence Research Unit Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia Wuyi University Jiangmen China

ISBN: (数字)9781728140827

ISBN: (纸本)9781728140834

In swarm robotics, motion coordination is being viewed as a whole system consists of multiple robots each with different velocity and path being optimized. This research focus on improve an optimization process called Particle Swarm Optimization (PSO) by altering the fitness function from static to dynamic. PSO has tendency to being trapped in local optima. By employing dynamic acceleration coefficient, the position of particles will be less dependent on the global best position if the global best fitness is found to be saturated in the region and hence release the swarm to another region for searching in other unexplored region. Nominal, dynamic and extreme scenarios were employed to test the performance between PSO and dynamic coefficient PSO. The different on expected optimization time between DCPSO and PSO for nominal condition is DCPSO lead only 0.26 second. While in dynamic condition the different is DCPSO lead 1.09 second. For extreme condition, the different become significant, which DCPSO lead 3.58 second.

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Enhancement of Ant Colony Optimization in Multi-Robot Source Seeking Coordination

Enhancement of Ant Colony Optimization in Multi-Robot Source...

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IEEE Conference on Systems, Process & Control (ICSPC)

作者： Jun Wei Lee Nyiak Tien Tang Kit Guan Lim Min Keng Tan Baojian Yang Kenneth Tze Kin Teo Modelling Simulation & Computing Laboratory Artificial Intelligence Research Unit Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia Deparment of Intelligent Manufacturing Wuyi University Jiangmen China

ISBN: (数字)9781728118260

ISBN: (纸本)9781728118277

关键词： Heuristic algorithms Optimization Urban areas Mobile robots Ant colony optimization Mathematical model

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Adapting Perturbation Voltage in PV Array with Power Point Tracking and Differential Evolution

Adapting Perturbation Voltage in PV Array with Power Point T...

引用

IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS)

作者： Nurul Izyan Kamaruddin Ahmad Razani Haron Min Keng Tan Soo Siang Yang Hui Hwang Goh Kenneth Tze Kin Teo Modelling Simulation & Computing Laboratory | Artificial Intelligence Research Unit Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia School of Electrical Engineering Guangxi University Nanning China

ISBN: (数字)9781728140827

ISBN: (纸本)9781728140834

The power output of photovoltaic array may reduce as the photovoltaic panels' connection close to each other and lead to the occurrence of partials shading. Under partial shading condition, the unshaded modules of PV array receive solar irradiation at higher level, while the shaded module of PV array receives lower irradiation. Thus, multiple maximum power point also will occur under the partial shading condition. The proposed modelling in this paper is 4×4 PV array and tested two different conditions in partial shading condition (PSC) where there are random irradiance values that had been set. Therefore, this paper aim to model more than one PV array and the platform modelling for this paper is 4×4 PV array. Besides that, explore the conventional method which is Perturb and Observe (P&O) based MPPT controller in optimizing the efficiency of the PV array. Unfortunately, the conventional method only can trap the power at the local maximum power under partial shading conditions. Thus, the proposed algorithm that used to track the maximum power is differential evolution (DE) in order to improve the power output of the PV array. The simulation results shows the proposed DE able to track the global MPP.

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Time-periodic steady-state solution of fluid-structure interaction and cardiac flow problems through multigrid-reduction-in-time

arXiv

引用

arXiv 2021年

作者： Hessenthaler, Andreas Falgout, Robert D. Schroder, Jacob B. de Vecchi, Adelaide Nordsletten, David Röhrle, Oliver Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Pfaffenwaldring 5a Stuttgart70569 Germany Stuttgart Center for Simulation Technology University of Stuttgart Pfaffenwaldring 5a Stuttgart70569 Germany Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94551 United States Department of Mathematics and Statistics University of New Mexico AlbuquerqueNM87131 United States School of Biomedical Engineering and Imaging Sciences King’s College London 4th FL Rayne Institute St. Thomas Hospital LondonSE1 7EH United Kingdom Department of Biomedical Engineering and Cardiac Surgery University of Michigan NCRC B20 2800 Plymouth Rd Ann Arbor48109 United States

In this paper, a time-periodic MGRIT algorithm is proposed as a means to reduce the time-to-solution of numerical algorithms by exploiting the time periodicity inherent to many applications in science and engineering. The time-periodic MGRIT algorithm is applied to a variety of linear and nonlinear single- and multiphysics problems that are periodic-in-time. It is demonstrated that the proposed parallel-in-time algorithm can obtain the same time-periodic steady-state solution as sequential time-stepping. It is shown that the required number of MGRIT iterations can be estimated a priori and that the new MGRIT variant can significantly and consistently reduce the time-to-solution compared to sequential time-stepping, irrespective of the number of dimensions, linear or nonlinear PDE models, single-physics or coupled problems and the employed computing resources. The numerical experiments demonstrate that the time-periodic MGRIT algorithm enables a greater level of parallelism yielding faster turnaround, and thus, facilitating more complex and more realistic problems to be solved. Copyright © 2021, The Authors. All rights reserved.

关键词： Fluid structure interaction

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