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Efficient Transmission based on Genetic Evolutionary Algorithm 4

Efficient Transmission based on Genetic Evolutionary Algorit...

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

作者： Fan, Jin Lim, Kit Guan Ee Chuo, Helen Sin Tan, Min Keng Farzamnia, Ali Kin Teo, Kenneth Tze Modelling Simulation & Computing Laboratory Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Today's society has grappled with the age of big data. Widespread use of informatization technology has promoted the development of artificial intelligence and communication technologies, which can play an important role in communication networks. In this paper, an energy-saving mechanism based on genetic algorithm in wireless sensor network (WSN) is proposed. The basic working principle and main characteristics of genetic algorithm (GA) are summarized, and the theory, technology and existing problems of GA are discussed. Through the analysis of the transmission efficiency of GA, a new genetic evolutionary algorithm combined with the characteristics of ant colony algorithm (ACO) is proposed. Through the simulation of the transmission performance of genetic optimization algorithm, the comparison of transmission energy consumption between GA and evolutionary algorithm is analyzed, and the evolutionary algorithm with higher transmission performance is obtained. Results showed that the proposed hybrid genetic algorithm with ant colony optimization (GACO) delivers 78.70% and 73.51% lower number of transmission failures than GA and ACO respectively. © 2022 IEEE.

关键词： Genetic algorithms

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Maximizing Power Generation in Variable Speed Micro-Hydro with Power Point Tracking 4

Maximizing Power Generation in Variable Speed Micro-Hydro wi...

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

作者： Tan, Min Keng Javinez, Norafe Maximo Lim, Kit Guan Haron, Ahmad Razani Ibrahim, Pungut Kin Teo, Kenneth Tze Modelling Simulation & Computing Laboratory Faculty of EngineeringUniversiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

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). © 2022 IEEE.

关键词： Genetic algorithms

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On-Demand Priority Traffic Optimizer with Fuzzy Logic Microcontroller 4

On-Demand Priority Traffic Optimizer with Fuzzy Logic Microc...

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

作者： Ee Chuo, Helen Sin Seah, Yee En Tan, Min Keng Lim, Kit Guan Liau, Chung Fan Kin Teo, Kenneth Tze Modelling Simulation & Computing Laboratory Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Current traffic control system in Malaysia is developed based on predetermined setup, where the system is not able to analyse the surrounding condition to optimize the green time. When there is an unusual traffic flow, the control system fails to control traffic flow efficiently, causing delays and requiring the assistance of traffic police. The main objective of this project is to explore the potential of fuzzy logic embedded control system in optimizing the traffic congestion corresponding to the priority traffic signal. The developed real time traffic-adaptive control system operates by prioritising the green light based on the received priority signals such as high flow rate phases and the emergency vehicles. A microcontroller-based traffic controller with computed algorithm was developed. The performance of the controller in reducing average waiting time and average vehicle queue length at a traffic intersection was evaluated. In overall, Fuzzy Logic managed to reduce 23% of average waiting time and 11% of average vehicles in queue at the intersection as compared to the conventional control. © 2022 IEEE.

关键词： Fuzzy logic

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Adapting Perturbation Voltage For Variable Speed Micro-Hydro Using Particle Swarm Optimization (PSO) 4

Adapting Perturbation Voltage For Variable Speed Micro-Hydro...

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

作者： Lim, Kit Guan Zainal, Mohd Izzat Fikri Md Tan, Min Keng Haron, Ahmad Razani Chai, Chang Yii Teo, Kenneth Tze Kin Universiti Malaysia Sabah Faculty of Engineering Modelling Simulation & Computing Laboratory Kota Kinabalu Malaysia

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

The aim of this research is to explore a technique that can be implemented to the Variable Speed Micro-hydro Power Generation (VS-MHPG) system to search the optimum operating point for the maximum power extraction. Micro-Hydro that operates in variable speed mode are sensitive to the changes of flow rate and proved to have wide operating point. The Perturb and Observe (P&O) based maximum power point tracking (MPPT) was applied to the VS-MH and based on simulation. However, oscillation occur at maximum point due to the large perturbation speed. The existing Micro-hydro Power Generation (MHPG) system commonly suffers from the non-optimal input control as the controller estimate the changes of flow rate without anticipating the global maximum power curve. Hence the implementation of P&O based MPPT is expected to improve the efficiency of MHPG system while reducing the fluctuation of output power. Results show that the value of perturbation speed affects the performance of MPPT algorithm to search the maximum operating point. Low perturbation signal requires many numbers of iteration before it reaches the steady state. Meanwhile, high perturbation signal will cause the fluctuation that led to unstable power production. Thus, new method was introduced which is Particle Swarm Optimization (PSO) that is expected to improve the performance of conventional MPPT. simulation result shows that PSO based MPPT was able to track the global maximum point under extreme condition with no power fluctuation compared to P&O MPPT. Also, PSO based MPPT provides adaptive perturbation speed that show improvement in maximum power tracking by 20.88%. © 2022 IEEE.

关键词： Speed

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Ant Colony Optimization Based Multi-Hop Wireless Transmission for Enhanced Network Reliability 6

Ant Colony Optimization Based Multi-Hop Wireless Transmissio...

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6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Lim, Kit Guan Bin Sakimin, Mohd Fikry Fazri Wei, Longxin Tan, Min Keng Yang, Soo Siang Teo, Kenneth Tze Kin 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

ISBN: (纸本)9798350389692

Wireless multi-hop transmission is a new technology that has been used for data transfer in no internet connectivity and electricity area. Wireless sensor networks are being created for many different purposes, both for everyday civilian uses and military applications. Multi-hop transmissions are popular with challenges like shape of terrain, low data transfer rate and loss connectivity easily when it comes to design. The main problem is current control for routing in multi-hop wireless sensor networks works as what it has been initialized and the nodes only communicate with preset nodes. Malfunction of a single transceiver can cause connection losses and decrease network reliability. This report will explore the optimization of multi-hop wireless communication by implementing ACO in omnidirectional transceiver. The aim is to develop wireless sensor network model, compute control strategy for routing in random topology, to optimize wireless transmission by implementing artificial intelligence and analyze the performance of proposed algorithm. To achieved these goals, a simulation has been designed in MATLAB to represent the real situation virtually. Several parameters such as number of nodes, area size and transmission range must be considered in the simulation to get optimum result. Conventional routing protocol such as Distance-Vector is used as a comparison to ACO control strategy with the help of Euclidean distance formula. This project focus on optimizing routing strategy from source node to target node without considering pathloss and line of sight. Then, the results are performance comparison of Distance-Vector, Randomized Multi-Path and Ant Colony Optimization in 100 iterations. After simulation, Ant Colony Optimization does improve Distance-Vector by increasing network lifetime with 1st 'dead node' appear at 100th iteration but still maintaining as least average hops path as possible. The number of hops and 'dead node' for a single complete transmission fro

关键词： MATLAB

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Fuzzy Logic Based Perturb-and-Observe Algorithm for Minimizing Output Fluctuation of a PV System 5

Fuzzy Logic Based Perturb-and-Observe Algorithm for Minimizi...

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

作者： Tan, Min Keng Moloney, Braindane Owin Lim, Kit Guan Yang, Soo Siang Goh, Hui Hwang Kin Teo, Kenneth Tze Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia School of Electrical Engineering Guangxi University Nanning China

ISBN: (纸本)9798350304152

Photovoltaic systems generate direct current (DC) power by absorbing sunlight through photovoltaic cells. However, under non-uniform irradiance, multiple peaks in the Maximum Power Point Tracking (MPPT) pose challenges for precisely tracking the global maximum power point. Traditional methods often focus on local maximum power points instead of the global ones. This research aims to enhance the Perturb and Observe (P&O) approach and model the PV module to accurately track the Maximum Power Point (MPP). The study investigates the performance of PV arrays using models with non-uniform characteristics. While traditional MPPT techniques like perturb and observe (P&O) can track the MPP, they struggle with non-uniform irradiance, leading to output power fluctuations and losses. simulation results demonstrate that the P&O-based MPPT algorithm, tailored for maximum power production, adjusts the operational point effectively. Comparing the conventional MPPT output voltage 17.8V under non-uniform irradiance of conventional P&O MPPT output voltage and a non-conventional Fuzzy Logic 33.8V under non-uniform irradiance indicates that the Fuzzy MPPT achieves 52.66% of the output voltage obtained by the non-conventional MPPT comparing to conventional MPPT. These algorithms offer improved precision and flexibility, although their implementation may require higher processing power and computational capabilities. Therefore, the next focus will be on developing a fuzzy logic based MPPT and enhancing the photovoltaic system. © 2023 IEEE.

关键词： Fuzzy logic

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Adaptive Exponential Feeding Control for a Fedbatch Yeast Fermentation Process Using Reinforcement Learning 6

Adaptive Exponential Feeding Control for a Fedbatch Yeast Fe...

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6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Sodiwal, Khairul Aznizam Chai, Wan Ying Tan, Min Keng Kin Teo, Kenneth Tze Tham, Heng Jin Chemical Engineering Programme Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu88400 Malaysia Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9798350389692

The fermentation process is complex and nonlinear because the growth pattern of microorganisms is sensitive to environmental conditions. Exponential feeding (EF) is a common and simple control strategy for regulating substrate feed flow rates using a pre-determined calculation based on growth kinetics. However, this method does not adapt to changes in the fermentation process. Reinforcement Learning (RL) is a subset of Artificial Intelligence (AI) that focuses on teaching optimal behaviors through interaction with an environment using rewards or penalties. This work explores an Adaptive Exponential Feeding (AEF) control using RL to determine the gain value of the feeding rate, aimed at maximizing yeast concentration and minimizing the by-product formation in a fed-batch baker's yeast fermentation. The kinetic and dynamic behavior of the fermentation process was modeled and simulated using MATLAB. The AEF approach uses an RL agent to optimize the feeding rate by interacting with the fermentation environment and selecting actions based on yeast concentration, glucose levels, and ethanol changes. The gain value ranges from 0 to 2.5 with a 0.1 interval. After iterations, the optimal policy is obtained. simulations with four different initial yeast and substrate concentrations showed that AEF significantly improved the fermentation performance, yielding a higher yeast concentration of 64.86 g/L compared to 44.68 g/L with EF, a 45.17 % increase, and reducing ethanol formation by 0.15 g/L. This adaptive approach demonstrates potential for enhanced efficiency and productivity in industrial fermentation processes. © 2024 IEEE.

关键词： Reinforcement learning

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Improve Accuracy of VGG-16 Neural Network Based Face Detection with Data Augmentation

Improve Accuracy of VGG-16 Neural Network Based Face Detecti...

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

作者： Min Keng Tan Jun Shen Chin Renee Ka Yin Chin Lorita Angeline Chung Fan Liau Kenneth Tze Kin Teo Modelling Simulation & Computing Laboratory Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

A face detection model can be used to detect the presence of a face in real time detection with great accuracy. However, the face detection model can only perform well under a very optimum condition and situation which makes it unreliable and unrealistic as it should be able to perform detection under normal conditions. Geometric transformation, color transformation and even kernel filters will affect the face detection model and will cause the detection model to not function and cause misclassification. A convolutional neural network (CNN) of Visual Geometry Group 16 (VGG-16) will be utilized to train the dataset and optimize the face detection model. The objective of this work is to mitigate the high loss and misclassification by performing image augmentations towards the data and analyze which augmentations parameters are ideal for the face detection model. The mixed augmentation selection method was proposed to solve the problem of not being able to detect face under different conditions. The result of different parameters and augmentations selections have been analyzed and compared and upon fine-tuning of parameters, the accuracy can be increase to 96.4% from the initial 74.4%.

关键词： Training Accuracy Fluctuations Filters Image color analysis Neural networks Data augmentation Face detection Kernel Faces

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Minimizing Energy Expenditures in Multi-Hop Transmissions with Genetic Algorithm 5

Minimizing Energy Expenditures in Multi-Hop Transmissions wi...

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

作者： Lim, Kit Guan Chin, Qi Yang Tan, Min Keng Farzamnia, Ali Wei, Longxin 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

ISBN: (纸本)9798350304152

Wireless Sensor Networks (WSN) are placed in a specific field to gather, analyse, and collect data before sending it to the base station via various communication techniques and application layer protocols. However, a bottleneck in internet-of-things-based wireless sensor networks deployment is their limited bandwidth, battery power, and computing power capability. The major reason for low energy consumption and efficiency requirement is that WSNs are deployed in remote inaccessible locations and left alone and hence are difficult to recharge. The second reason is wireless sensors or Internet of Things (IoT) sensors are inherently power limited, as designed by manufacturers. This reduces the network lifetime and makes it a critical issue along with energy management. There are lots of algorithms that have been researched like Low Power Adaptive Clustering Hierarchy (LEACH), and LEACH variants such as Low Power Adaptive Clustering Hierarchy-Centralized (LEACH-C), Hybrid Energy Efficient Distributed (HEED), Power Efficient Gathering in Sensor Information Systems (PEGASIS), etc. In this study, existing literature are reviewed, and several routing algorithms are analysed to see how Cluster Head (CH) selection varies between them. An overview of methods influenced by genetics and nature-inspired algorithms and game theory routing protocol implementations are also included. The results are studied and compared on the state of the art for routing algorithms and an algorithm that presents an improvement over the previous reviewed works is proposed. The results show that genetic algorithm LEACH (GA-LEACH) outperformed LEACH and modified LEACH (MOD LEACH) in terms of performance. Comparing with LEACH and MODLEACH, GALEACH greatly enhanced the network lifetime and improved energy consumption by 34.75% and 14.61% respectively. © 2023 IEEE.

关键词： MATLAB

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Non-Intrusive Biomass Estimation in Aquaculture Using Structure from Motion Within Decision Support Systems 6

Non-Intrusive Biomass Estimation in Aquaculture Using Struct...

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6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Tang, Nyiak Tien Lim, Kit Guan Yoong, Hou Pin Ching, Fui Fui Wang, Tianlei Teo, Kenneth Tze Kin Borneo Marine Research Institute Universiti Malaysia Sabah Kota Kinabalu Malaysia Wuyi University Faculty of Intelligent Manufacturing Jiangmen China Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9798350389692

This paper details an advanced implementation of Structure from Motion (SfM) techniques within Decision Support Systems (DSS) for non-intrusive fish weight measurement in Precision Fish Farming (PFF). Unlike conventional methods that often require physical handling or intrusive sensors, this study utilizes a synchronized multi-camera array strategically positioned underwater to capture high-resolution images across multiple viewpoints. Employing sophisticated algorithms, the research meticulously handles image distortion typical in underwater environments through tailored rectification processes, enhancing feature detection and matching accuracy under variable lighting and turbidity. Our approach integrates dense point cloud generation and sophisticated 3D mesh reconstruction to derive volumetric measurements of fish, which are then correlated with biomass estimations through empirical models refined with machine learning techniques for predictive accuracy. Initial validations against standard physical weighing methods demonstrate a significant enhancement in measurement precision and repeatability, with a reduced error margin by over 30%. These advancements not only streamline operational efficiencies but also pave the way for more sustainable fish farming practices by minimizing human-fish interactions and the associated stress-induced behavioral alterations. © 2024 IEEE.

关键词： Machine learning

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