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检索条件"机构=Laboratory of Modelling and Simulation in Engineering"

共 347 条记录，以下是1-10 订阅

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Improve Accuracy of VGG-16 Neural Network Based Face Detection with Data Augmentation 6

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

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

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

ISBN: (纸本)9798350389692

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%. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimization of Photovoltaic Energy Harvesting Using Clonal Selection Algorithm 5

Optimization of Photovoltaic Energy Harvesting Using Clonal ...

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

作者： Tan, Min Keng Joponi, Grace Butiza Lim, Kit Guan Haron, Ahmad Razani Chai, Chang-Yii Kin Teo, Kenneth Tze Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9798350304152

The rising demand for renewable energy sources has fueled extensive research in photovoltaic (PV) systems. However, conventional Maximum Power Point Tracking (MPPT) algorithms often encounter challenges when tracking the global maximum power point under non-uniform irradiance conditions. To address this issue, the Clonal Selection Algorithm (CSA) is proposed as an effective approach to enhance MPPT algorithm performance. The CSA dynamically adjusts the voltage perturbation size based on instant ambient irradiance and temperature, leading to improved global maximum power point tracking and enhanced efficiency in PV systems. Experimental results demonstrate the superiority of the proposed CSA over conventional MPPT algorithms, especially in scenarios with varying solar irradiance. The CSA's adaptability allows PV systems to operate closer to their optimal efficiency, maximizing energy harvest from available solar resources. Overall, this research contributes valuable insights into sustainable and efficient energy solutions by leveraging the capabilities of the CSA. Successfully integrating the CSA in PV systems plays a critical role in establishing an eco-friendly and resilient renewable energy infrastructure, for a greener future. © 2023 IEEE.

关键词： Renewable energy resources

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simulation and Batch modelling of Microbial Biosurfactant Production 5

Simulation and Batch Modelling of Microbial Biosurfactant Pr...

引用

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

作者： Ee Chuo, Helen Sin Ramlan, Andy Raymond Lim, Kit Guan Tan, Min Keng Yoong, Hou Pin Kin Teo, Kenneth Tze Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9798350304152

This project concentrates on simulating the batch microbial biosurfactant production in crude oil and its interfacial tension response. Bacterial metabolism has been widely studied for its significant role in bioremediation of oil spill, which is proven capable of reducing the interfacial tension between oil and water. Therefore, this simulation is integrated to observe the metabolic behaviour of the bacteria of interest upon its uptake of carbon source from crude oil., biosurfactant production and the system's interfacial tension which are some of the outputs mainly being discussed within the culture system. The metabolic process in the crude oil batch is first modelled, then simulated in MATLAB software, and the results are verified through literature and process theories, while the model was validated using the experimental data. The simulation will use a step size of 1/60 h, equivalent to 60 seconds, set to a total of 11520 iterations for a running time of 8 days. Different crude oil substrate concentrations are set to 1.5 g/L, 2.0g/L, 2.5g/L, 3.0g/L and 3.5 g/L for parameter testing and the initial concentrations of biosurfactant and cell concentration are 0.02 g/L and 0.02 g/L respectively, while the initial interfacial tension is set at 70.8933 mN/m. The simulation outcome in this study signifies that the final concentrations of biosurfactant and bacteria cell increases with the increase in substrate concentration as crude oil degrades completely quicker with substrate concentration increase among culture batches obtained from the parameter testing. Additionally, the simulated interfacial tension profile plotted against biosurfactant from experimental research., showed similar profiles across batch cultures with an increasing substrate concentration where IFT is reduced from 70 ± 0.5 mN/m to 38 ± mN/m, but exhibited decreasing value of biosurfactant concentration defined at 0.5 micelle concentration (based on experimental value). © 2023 IEEE.

关键词： Bioremediation

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Multi-Area Load Frequency Control Using Particle Swarm Optimization 5

Multi-Area Load Frequency Control Using Particle Swarm Optim...

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

作者： Tan, Min Keng Lim, Wei Han Lim, Kit Guan Kow, Wei Yeang Ng, Min Sian Kin Teo, Kenneth Tze Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9798350304152

Load Frequency Control is indeed important in any country that operates an interconnected power grid for regulating system frequency by adjusting the generation to match the load demand. To maintain the power system stability and improve efficiency in scheduling, this paper proposes Two-area Load Frequency Control (LFC) Optimization with Particle Swarm Optimization. The design process begins with understanding the fundamental concepts, starting from Automotive Generation Control in a single-area power system design and progressing to LFC design in a two-area power system. The two-area power system consists of Tie-line Bias Control (TLBC) for maintaining zero tie-line power and Area Control Error (ACE) for stabilizing system frequency. To achieve fitness results in simulations, this paper presents three modeling designs: conventional design, controller design, and controller design with PSO. The controller system with PSO leads an evaluation of 53.4 % time schedule in system frequency performance and an evaluation of 76.9% time schedule in generation power deviation with respect to the conventional model. In addition, the controller system lags an evaluation of 70.0% time schedule in system frequency performance and an evaluation of a 78.7% time schedule in generation power deviation with respect to the conventional model. © 2023 IEEE.

关键词： Controllers

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Adaptive Traffic Signal Control using Genetic Algorithm for a 2×2 Traffic Network 6

Adaptive Traffic Signal Control using Genetic Algorithm for ...

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

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

ISBN: (纸本)9798350389692

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× 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. © 2024 IEEE.

关键词： Genetic algorithms

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AloT-Driven Machine Learning for Anomaly Detection in Structural Health Monitoring 6

AloT-Driven Machine Learning for Anomaly Detection in Struct...

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

作者： Angeline, Lorita Lim, Kit Guan Tan, Min Keng Uda, Muhd. Nur Afnan Jenarun, Joaquim Abel Teo, Kenneth Tze Kin Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9798350389692

Structural health monitoring (SHM) is crucial for real-time assessment of structures to preemptively detect anomalies that may lead to failures. This study explores the integration of Artificial Intelligence of Things (AIoT) principles to enhance anomaly detection using acceleration and gyroscopic sensor data. The study involved setting up hardware comprising acceleration and gyroscopic sensors, integrated with a web server for real-time data monitoring. Issues with the raw dataset, including noise and variability, were addressed through preprocessing techniques. Four machine learning models, Support Vector Machine (SVM), Random Forest, Gradient Boosting, and Neural Network were evaluated on preprocessed data, focusing on recall as critical for anomaly detection in SHM. Models trained on reduced datasets showed varied performance compared to those using large, raw datasets, which, while computationally intensive, exhibited similar performance. SMOTE significantly improved recall, achieving 93.94% in Random Forest after correcting class imbalance and reducing dataset size. The findings underscore the potential of AIoT-driven machine learning approaches, to significantly enhance SHM modelling. By leveraging these technologies, infrastructure management can be fortified with more reliable anomaly detection capabilities, ensuring heightened safety and resilience in the AIoT era. © 2024 IEEE.

关键词： Gyroscopes

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Effect of Cooling Channel Structure on Permanent Magnet Synchronous Motor 6

Effect of Cooling Channel Structure on Permanent Magnet Sync...

引用

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

作者： Huang, Zhencai Chen, Hui Lim, Kit Guan Tan, Min Keng Chua, Bih Lii Teo, Kenneth Tze Kin Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia School of Automotive Engineering Liuzhou Polytechnic University Liu Zhou China

ISBN: (纸本)9798350389692

Taking the 135kW magnet synchronous motor as the research object, the CFD simulation was used to analyze the influence of spiral-type runner, single-channel circumferential Z-type runner, and axial Z-type runner on the heat dissipation performance of the motor. The maximum temperature of the inner wall of the shell is 358.93K, compared with the single-channel circumferential Z-type runner and axial Z-type runner decreased by 1.69K, 10.65K and 46.47K, respectively. The velocity uniformity and pressure loss of the helical runner is optimal, and the coefficient of velocity uniformity and the difference between import and export pressure are 0.948 and 14.78kpa, respectively;the runner structure of the PM synchronous motor can be preferred to consider the use of the helical runner form. © 2024 IEEE.

关键词： Permanent magnets

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Impact of Helical Channel Number on Synchronous Motor Cooling Performance 6

Impact of Helical Channel Number on Synchronous Motor Coolin...

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

作者： Meng, Xiang Lei Chen, Hui Tan, Min Keng Lim, Kit Guan Chua, Bih Lii Te, Kenneth Tze Kin Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia School of Automotive Engineering Liuzhou Polytechnic University Liu Zhou China

ISBN: (纸本)9798350389692

In order to deeply explore the specific influence of the number of helical channels on the cooling performance of a motor, we carefully constructed a three-dimensional fluid-solid coupling calculation model of the flow channel with the help of CFD simulation technology. For the helical flow channels of the 135-k water-cooled permanent magnet synchronous motor shell, we conducted detailed numerical simulation calculations of the temperature field, velocity field, and pressure field. The results show that the number of helical channels has a direct impact on the cooling performance of the motor. Specifically, when the number of channels increases from 3 to 7, the maximum temperature on the inner wall of the shell decreases significantly by 94.41-k, and the velocity uniformity coefficient also increases by 0.05. However, this change also leads to an increase in the flow resistance within the channels, resulting in an increase in the pressure difference between the inlet and outlet of the channels by 4.73-kpa. These findings provide powerful data support for us to further optimize motor design and improve cooling performance. © 2024 IEEE.

关键词： Permanent magnets

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Yield Optimization for Exothermic Batch Process Using Particle Swarm Optimization 6

Yield Optimization for Exothermic Batch Process Using Partic...

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

作者： Tan, Min Keng Bansing, Matthew Laurentius Xu, Huiyi Chuo, Helen Sin Ee Tham, Heng Jin Teo, Kenneth Tze Kin Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia School of Marine Science and Engineering Hainan Tropical Ocean University Hainan China

ISBN: (纸本)9798350389692

Modern advancements in optimizing processes have been explored by various researchers, particularly for exothermic batch processes, which are prolific in the chemical and pharmaceutical industries. This paper proposes using artificial intelligence, specifically particle swarm optimization (PSO), to enhance these exothermic batch processes. Given that most exothermic batch processes utilize generic model control (GMC), the PSO algorithm will primarily focus on optimizing the control of the GMC within the process. The precise control of the controllers will aim to maintain the reactor temperature during the processes, ensuring stability and efficiency. This approach leverages AI to improve process performance, highlighting the potential of PSO in industrial applications. The proposed algorithm is shown to be able to produce a result of an increase of 4.5% of production of the desired product, while decreasing by-product production of 97%. The time it takes for the production of the desired product is also improved by 11.8%. This shows an efficiency increase of the entire exothermic batch processes, The results show that an improvement of the efficiency of the process makes the proposed algorithm a great method to improve the controller of the processes. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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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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