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Evolutionary Based Control and Optimisation of Exothermic Batch Process 5

Evolutionary Based Control and Optimisation of Exothermic Ba...

引用

5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023

作者： Xu, Huiyi Ee Chuo, Helen Sin Tan, Min Keng Ng, Chi Huey Yang, Min Kin Teo, Kenneth Tze Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia Hainan Tropical Ocean University Marine Electrical and Electronic Engineering Hainan China Wuyi University Faculty of Intelligent Manufacturing Jiangman China

ISBN: (纸本)9798350304152

Control and optimization of exothermic batch reactions are crucial tasks in the field of chemical engineering, with extensive applications in various industries such as healthcare, monitoring, and production. This review comprehensively analyzes the state-of-the-art evolutionary algorithms used for controlling and optimizing exothermic batch reactions. The paper explores and compares the performance of popular algorithms such as GA, PSO, and other hybrid evolutionary algorithms. The review focuses on the strengths and limitations of each algorithm, analyzing their capabilities in handling different exothermic batch reaction processes, including their applicability, robustness, and complexity. Additionally, the paper investigates the commonly used optimisation techniques based on evolutionary algorithms for controlling exothermic batch reactions in practical case studies. This review also reveals the latest advancements and emerging trends in this field, such as multi-objective optimization, multi-mode optimization, and the integration of deep learning methods. © 2023 IEEE.

关键词： Deep learning

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Enhanced Multi-Hop Mechanism in Vehicular Communication System using Swarm Algorithm 3

Enhanced Multi-Hop Mechanism in Vehicular Communication Syst...

引用

3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021

作者： Lim, Kit Guan Teh, Ke Wen Tan, Min Keng Lago, Herwansyah Yang, Soo Siang Kin Teo, Kenneth Tze Modelling Simulation Computing Laboratory Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9781665428996

As on-road vehicles are increasing every year, safety on the road has become one of the major concerns. Therefore, Vehicular Ad Hoc Network (VANET) becomes as an important role on the road. In VANET, communication occurs between vehicles and the infrastructures. During broadcasting, an emergency message is transferred to the surrounding vehicles to alert other vehicles in the area. However, blind flooding in wireless network might result in redundant rebroadcast, contentions and collision with the neighbouring nodes. This situation is named as broadcast storm. Broadcast storm might lead to the losses of the information or lead to the wrong information being transmitted to the neighbouring nodes. The paper aims to design a broadcast control system which is able to optimize the broadcast process in VANET. Vehicular network is modelled in simulation of Urban Mobility (SUMO) and the algorithm is formulated in MATLAB. Data is extracted from SUMO through Traffic Control Interface for MATLAB (TraCI4Matlab). The broadcast protocol and Particle Swarm Optimization (PSO) algorithm are formulated in this paper. At the same time PSO is modified for the broadcast enhancement. Results showed that after parameters tuning the modified PSO is able to broadcast into a larger coverage area at a faster rate. © 2021 IEEE.

关键词： Vehicular ad hoc networks

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Optimization of Photovoltaic Energy Harvesting using Artificial Neural Network 3

Optimization of Photovoltaic Energy Harvesting using Artific...

引用

3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021

作者： Tan, Min Keng Lim, Norman Kamaruddin, Nurul Izyan Lim, Kit Guan Yang, Soo Siang Teo, Kenneth Tze Kin Modelling Simulation Computing Laboratory Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9781665428996

This paper proposes artificial neural network (ANN) based maximum power point tracking (MPPT) controller to maximize the energy harvested by a grid-connected photovoltaic (PV) system under various environmental conditions. Due to the non-linear characteristics, PV system will exhibit multiple peaks when the PV array receives non-uniform irradiance. As such, the conventional perturb and observe (PO) MPPT controller will be trapped at local maximum power point (MPP). Therefore, this paper aims to integrate ANN into MPPT controller to improve the effectiveness of the MPPT controller in tracking the global MPP. The effectiveness of the proposed method is tested under uniform and non-uniform irradiance conditions, and the performances are compared with the conventional PO. The simulation results show the proposed method able to track the global MPP even the PV system exhibits multiple peaks under non-uniform condition, whereas the conventional PO is trapped at local MPP. Thus, the proposed algorithm is able to harvest much energy as compared to the conventional method. © 2021 IEEE.

关键词： Neural networks

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Optimization of Signalized Traffic Network using Swarm Intelligence 3

Optimization of Signalized Traffic Network using Swarm Intel...

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3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021

作者： Tan, Min Keng Ladillah, Mohd.Riezman Chuo, Helen Sin Ee Lim, Kit Guan Chin, Renee Ka Yin Teo, Kenneth Tze Kin Modelling Simulation Computing Laboratory Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9781665428996

Traffic lights are the signaling devices located at a road intersection for granting right-of-way movement to road users. Thus, optimization of traffic signalization is essential to improve road service as it is the cost-effective way. Commonly, the signal optimization aims to minimize the average travel delay by manipulating the green signal timing. Besides to optimize the signal timing, the local intersection controller needs to collaborate with neighboring intersection controllers for minimizing the average delay for whole network as the congestion will be propagated to the downstream intersection. However, the current fixed-time signal controller is inadequate to manage the high growing demands of traffic as it is tuned offline using the nominal traffic flow data. Thus, this work aims to explore the potential of using Particle Swarm Optimization (PSO) to optimize the traffic signal timing for the traffic network. The proposed algorithm is texted using a benchmarked 1x2 traffic model and its performances are compared with the classical Genetic Algorithm (GA). The simulated results show the proposed PSO has improved the performances in minimizing average travel delay by 3.94 %. © 2021 IEEE.

关键词： Controllers

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Improved Q-Learning Algorithm for Path Planning of an Automated Guided Vehicle (AGV) 5

Improved Q-Learning Algorithm for Path Planning of an Automa...

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5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023

作者： Guo, Hao Tan, Min Keng Lim, Kit Guan Ee Chuo, Helen Sin Yang, Baojian Kin Teo, Kenneth Tze Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia Baotou Light Industry Vocational and Technical College Department of Automation Baotou City China Wuyi University Department of Intelligent Manufacturing Jiangmen China

ISBN: (纸本)9798350304152

With the development of modern industry 4.0, intelligent path planning is an essential research direction of schedule systems for an automated guided vehicle (AGV), which has been widely used in logistics distribution centers of enterprises. This work implements the improved Q-learning algorithm to solve the typical obstacle avoidance problems in path planning. Specifically, the conventional Q-learning algorithm has shortcomings including low operational efficiency and slow learning speed. The improved Q-learning algorithm is successively proposed by adding a learning process based on the original Q-learning algorithm, which enables AGV to find obstacles and target locations within the shortest time, thus improving the efficiency of path planning. Finally, the simulation experiments are carried out in the grid environment with MATLAB. In comparison to the conventional Q-learning algorithm, the improved Q-learning algorithm has faster convergence and higher learning efficiency, improved by 20%. © 2023 IEEE.

关键词： Motion planning

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Autonomous Path Optimization in Unfamiliar Map Through Deep Reinforcement Learning 5

Autonomous Path Optimization in Unfamiliar Map Through Deep ...

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5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023

作者： Wei, Longxin Lim, Kit Guan Tan, Min Keng Liau, Chung Fan Wang, Tianlei Kin Teo, Kenneth Tze Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia Fujian Polytechnic of Information Technology Faculty of the Internet of Things and Artificial Intelligence Fuzhou China Wuyi University Faculty of Intelligent Manufacturing Jiangmen China

ISBN: (纸本)9798350304152

The application of mobile robot's path planning and navigation in known map scenarios has been well-established. However, with the absence of a known map, the challenge lies in how a robot can effectively navigate through an unfamiliar environment and generate a sensible path towards its destination. This paper delves into the path planning quandary that mobile robot encountered in unknown environments. It employs a deep reinforcement learning (DRL) algorithm within a simulated environment, where the robot explores its objective. Additionally, an enhanced reward selection strategy is introduced through a performance-based comparison. This strategy tackles issues such as robot freezing and high collision rates during the exploration of uncharted territory. The outcome of this study is an enhancement in search performance and learning efficiency within path planning. The integration of this algorithm into the Robot Operating System (ROS) robot simulation platform, coupled with testing within the Gazebo simulation environment, demonstrates that the proposed strategy effectively diminishes obstacle collisions and boosts the success rate of reaching the intended destination. © 2023 IEEE.

关键词： Mobile robots

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Four Wheeled Mobile Robots: A Review 4

Four Wheeled Mobile Robots: A Review

引用

4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022

作者： Ling Leong, Jovina Seau Kin Teo, Kenneth Tze Yoong, Hou Pin Biomechatronics Research Laboratory Universiti Malaysia Sabah Faculty of Engineering Sabah Kota Kinabalu Malaysia Modelling Simulation and Computing Laboratory Universiti Malaysia Sabah Faculty of Engineering Sabah Kota Kinabalu Malaysia

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Wheeled mobile robots are becoming popular in recent years due to their applications that have impacted various aspects of daily life. With the advancement of technology, the demand for wheeled mobile robots especially four wheeled mobile robots has risen. The rise in demand had urged further research and development of more advanced wheeled mobile robots. In this paper, the recent development and focused on four wheeled mobile robots such as the types of wheels, types of steering systems, and control methods are reviewed. For the four wheeled mobile robots to have better steerability and superior cornering stability, the four-wheel steering systems are employed. Depending on the application, the used of standard or fixed wheels in four-wheel steering system also has better efficiency and accuracy compared to the Omni or mecanum wheels with similar maneuverability for indoor designs application. To effectively deal with the four-wheel steering mobile robot dynamic behavior, the payload uncertainty, system uncertainties and unknown disturbances including the parametric vibrations that lead to tracking performance limitations have to be overcome. For further improvement in tracking the performance of four-wheel steering mobile robots, the main issue is to tackle the payload uncertainty. An adaptive control method is proposed to overcome the issue and this method provided future research directions for the four-wheel steering mobile robot. © 2022 IEEE.

关键词： Mobile robots

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Advancing Weed Detection in Agricultural Landscapes Using Computer Vision 5

Advancing Weed Detection in Agricultural Landscapes Using Co...

引用

5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023

作者： Tan, Bei Ren Moung, Ervin Afnan Uda, Muhd. Nur Kin Teo, Kenneth Tze Angeline, Lorita School of Computer Science Taylor's University Subang Jaya Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9798350304152

This paper explores the need for weed detection in agricultural landscapes, where weed infestations pose significant crop productivity challenges. To address this matter, computer vision techniques are utilised to create a weed detection algorithm that accurately detects weed patches inside agricultural field images. To ensure reliable model evaluation, the dataset is split into training and testing subsets before being fed into the YOLOv3 model. The YOLOv3 model performs well in terms of precision, with an average precision of 66.4% for crop regions and a notable precision of 92.5% for detecting weeds. These results highlight the potential of artificial intelligence-driven weed detection techniques to optimize crop management practices and foster sustainable agriculture in the modern era. This research advances precision agriculture by effectively addressing weed infestations, paving the way for more efficient and targeted weed control strategies, which ultimately lead to increased crop yield and improved agricultural sustainability. © 2023 IEEE.

关键词： Computer vision

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Adaptive Traffic Signal Control using Genetic Algorithm for a 2×2 Traffic Network

Adaptive Traffic Signal Control using Genetic Algorithm for ...

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

作者： Min Keng Tan Shun Quan Chai Helen Sin Ee Chuo Kit Guan Lim Hui Hwang Goh Kenneth Tze Kin Teo Modelling Simulation & Computing Laboratory Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia School of Electrical Engineering Guangxi University Nanning China

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

This paper proposes an adaptive traffic signal control (ATSC) system optimized using genetic algorithm (GA) to reduce travel delays at multiple intersections. The growing urban population has caused significant traffic congestion, leading to wasted time, higher costs, and fuel waste. The main aim is to enhance traffic optimization performance by improving ATSC signal timings at multiple intersections, optimized using GA. A $2\times 2$ grid traffic network is developed to represent the multi-agent network, and the GA-based traffic optimizer is formulated to optimize traffic signals based on the traffic inflow rate. The performance of the GA-based signal optimizer is evaluated and compared with pretimed traffic control based on the average delay and queue length of vehicles passing through the intersections in undersaturated and saturated traffic. The simulations results show a decrease of 4.7 % (1.7 s) in delay and 6.8 % (0.6 pcu) in queue length for GA-based traffic control during undersaturated traffic scenarios, and a decrease of 18.7 % (8.4 s) in delay and 14.1 % (1.8 pcu) in queue length during saturated traffic scenarios. These results highlight the effectiveness of the GA-based signal optimizer in managing traffic flow in a multi-agent-based signalized traffic network. This paper provided a valuable contribution to the field of traffic signal optimization, particularly in urban settings where congestion is a significant issue.

关键词： Adaptive systems simulation Traffic control Delays Fuels Artificial intelligence Optimization Traffic congestion Genetic algorithms Testing

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A Safe Overtaking Control Scheme for Autonomous Vehicles using Rapid-Exploration Random Tree 4

A Safe Overtaking Control Scheme for Autonomous Vehicles usi...

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4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022

作者： Ma, Yincong Lim, Kit Guan Tan, Min Keng Ee Chuo, Helen Sin Angeline, Lorita Kin Teo, Kenneth Tze Modelling Simulation & Computing Laboratory Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia School of Computer Science Taylor's University Faculty of Innovation & Technology Subang Jaya Malaysia

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

In order to enhance the commuting ability of autonomous vehicles on the road and ensure the comfort and safety of passengers, the Rapid-exploration Random Tree (RRT) algorithm is applied to the research of safe overtaking control of autonomous vehicles. Firstly, the kinematics and dynamic model of the vehicle are implemented. Secondly, the RRT algorithm and the A-star algorithm are expounded, and the idea of the A-star algorithm is applied to the RRT algorithm for improvement. The improved algorithm is used to obtain the rough obstacle avoidance of the vehicle. The rough path is optimized by applying the cubic spline interpolation method to solve the problem that the path cannot be applied to the actual vehicle driving task. Finally, the simulation of the overtaking scheme is carried out. The results reveal that the safe overtaking scheme based on RRT algorithm achieves the predetermined requirements in the two actual cases under the premise of ensuring safety, controlling the swing of the sideslip angle of the vehicle's center of mass within a reasonable range. It has certain practical significance for ensuring the road safety of autonomous vehicles. © 2022 IEEE.

关键词： Autonomous vehicles

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