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Impact of Helical Channel Number on Synchronous Motor Cooling Performance 6

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

引用

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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Optimization of Crop Disease Classification using Convolution Neural Network 3

Optimization of Crop Disease Classification using Convolutio...

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

作者： Lim, Kit Guan Huong, Chii Soon Tan, Min Keng Liau, Chung Fan Yang, Min Teo, Kenneth Tze Kin Modelling Simulation Computing Laboratory Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia School of Intelligent Manufacturing Wuyi University Jiangmen China

ISBN: (纸本)9781665428996

This paper presents the deep learning model by Convolution Neural Network (CNN) in training the crop disease classifier via image classification. A camera will be equipped and applied in artificial intelligent drone to operate as a crop monitoring system used for agriculture. Agriculture productivity is a key component of country economy. Crop diseases can lead to a drop in the quality and quantity of agricultural products. Famers are facing problems to detect the crop diseases accurately in huge region of crops. Therefore, CNN based method for crop disease detection is proposed. Dataset contains of 16,257 color images which has a total of categories have been fed into the model, out of which 10 categories are of diseased crop leaves. The CNN model contains 7 convolution layers with the number of filters 32, 64, two layers with 128 filters, three layers with 256 filters and filter size $3\times 3$ is the proposed approach to perform crop disease classification, with the best testing accuracy of 99.02%. The crops are classified correctly using the suggested CNN design. The suggested CNN design is validated and evaluated which achieves accuracy of 99.02%, 0.98% error, 99% recall, 99% precision and 0.99 score of F-measure. In this paper, achievement of the proposed CNN model is reaching a promising result and simulated successfully in classifying the crop disease. © 2021 IEEE.

关键词： Neural network models

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Adaptive Route Optimization for Mobile Robot Navigation using Evolutionary Algorithm 3

Adaptive Route Optimization for Mobile Robot Navigation usin...

引用

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

作者： Lim, Kit Guan Lee, Yoong Hean Tan, Min Keng Yoong, Hou Pin Wang, Tienlei Teo, Kenneth Tze Kin Modelling Simulation Computing Laboratory Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia Wuyi University Department of Intelligent Manufacturing Jiangmen China

ISBN: (纸本)9781665428996

As technologies are advancing, demand for an intelligent mobile robot also increases. In autonomous robot design, the main problem faced by researchers is the path planning of mobile robot. Various kind of path planning algorithm was introduced in the past, but no algorithm has absolute superior towards the others algorithm. Classical methods like artificial potential field, grid search, and visual method have been easily overtaken by artificial intelligence due to its adaptability and ability to learn from the past mistakes or experience. For example, Ant Colony Optimization (ACO) is an optimization algorithm based on swarm intelligence which is widely used to solve path planning problem. However, the performance of ACO is highly dependent on the selection of its parameters. In this paper, the proposed adaptive ACO introduced two different ants, namely abnormal ant and random ant into the normal ACO to increase its global search ability and reduce the high convergence rate of ACO. Conventional ACO and adaptive ACO are compared in this paper and the results showed that adaptive ACO has better performance than conventional ACO in path planning. © 2021 IEEE.

关键词： Motion planning

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Mobile Machine Vision for Railway Surveillance System using Deep Learning Algorithm 3

Mobile Machine Vision for Railway Surveillance System using ...

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

作者： Lim, Kit Guan Siruno, Daniel Tan, Min Keng Liau, Chung Fan Huang, Sha Teo, Kenneth Tze Kin Modelling Simulation Computing Laboratory Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia School of Rail Transportation Wuyi University Jiangmen China

ISBN: (纸本)9781665428996

Trains have been a popular transportation in our daily life. However, there is no proper surveillance system for obstacle detection at the railway, leading to the happen of unwanted accidents. In order to overcome this issue, machine vision embedded with deep learning algorithm can be implemented. Obstacle detection can be achieved through vision-based object detection, where the object classification model computes the images similarity to its respective classes, classifying its potential as an obstacle. In this paper, object detection model is developed and implemented with deep learning algorithm. Object classification model is produced through the model training with Deep Neural Networks (DNN). The detection model used in this paper is Single-Shot multibox Detection (SSD) MobileNet detection model. This model can be implemented with Raspberry Pi to simulate the object detection algorithm virtually. During simulation, the object recognition algorithm is able to detect and classify various objects into its respective classes. By applying past research approaches, the developed object detection model is able to analyze image as well as real-time video feed to identify multiple objects. Any object that has been detected at the Region of Interest (ROI) can be characterized as an obstacle. © 2021 IEEE.

关键词： Object detection

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The effect of rotation speed and mixer size on granular flow and mixing in bladed mixers 9

The effect of rotation speed and mixer size on granular flow...

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9th International Conference on Micromechanics on Granular Media, Powders and Grains 2021

作者： Herman, Angga Gan, Jieqing Yu, Aibing Laboratory of Simulation and Modelling of Particulate System ARC Hub for Computational Particle Technology Department of Chemical Engineering Monash University MelbourneVIC3800 Australia

Bladed mixers are widely used in the industry for granular mixing. In the past decades, the mixing of particles in bladed mixers has been extensively investigated experimentally and numerically. Recently, GPU-based DEM has been employed to simulate various industrial-scale applications. This work aims to apply the GPU-based DEM to investigate the effect of rotation speed and mixer size on granular mixing in bladed mixers of different sizes. The simulation in a larger mixer revealed distinct particle flow patterns that well-describe the mixing mechanism which is difficult to observe in a smaller mixer. The Lacey’s mixing index curves revealed a delay in mixing as the mixer size increases. The mixing rate decreases as the mixer size increases and it can be improved with increasing rotation speed. The average particle velocity increase significantly with increasing rotation speed and mixer size. © The Authors, published by EDP Sciences.

关键词： Mixers (machinery)

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Four Wheeled Mobile Robots: A Review 4

Four Wheeled Mobile Robots: A Review

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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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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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Energy-Efficient Ant Colony Based LEACH Routing Algorithm in Wireless Sensor Network 4

Energy-Efficient Ant Colony Based LEACH Routing Algorithm in...

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

作者： Abd Latif, Muhammad Zahir Lim, Kit Guan Tan, Min Keng Ee Chuo, Helen Sin Wang, Tienlei Kin Teo, Kenneth Tze Modelling Simulation & Computing Laboratory Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia Wuyi University Department of Intelligent Manufacturing Jiangmen China

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Wireless sensor network (WSN) is a network consist of multiple sensor nodes which sense and transmit data to a base station for data collection. Since the nodes in WSN are battery powered devices and nodes loss energy mostly due to transmission, an energy efficient routing protocol is needed to reduce the nodes energy consumption and prolong the network lifetime. However, existing routing protocols produce some drawbacks such as selection of cluster head with low residual energy and formation of different sizes of cluster which leads to uneven energy consumption among the nodes. Therefore, an Ant Colony Optimization based Low Energy Adaptive Clustering Hierarchy (LEACH-ACO) protocol for energy efficient transmission in WSN is proposed in this paper. This research aims to develop a routing protocol that utilize the existing LEACH routing protocol to improve the performance of WSN and prolong the lifetime of the network. The results showed that LEACH-ACO protocol outperformed Direct Transmission (DT) and LEACH protocol in terms of network lifetime, energy consumption and energy per transmission. © 2022 IEEE.

关键词： Ant colony optimization

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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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CANTILEVER FREE MAGNETIC ACTUATION FOR MULTISTABLE VERTICAL DISPLACEMENT

CANTILEVER FREE MAGNETIC ACTUATION FOR MULTISTABLE VERTICAL ...

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MikroSystemTechnik Kongress 2023: Mikroelektronik, Mikrosystemtechnik und ihre Anwendungen - Nachhaltigkeit und TechnologiesouveranitatMicroSystems Technology Congress 2023: Microelectronics, Microsystems Technology and their Applications - Sustainability and Technology Sovereignty

作者： Weber, Pascal M. Schütz, Arwed Bechtold, Tamara Wallrabe, Ulrike Laboratory for Microactuators IMTEK - Department of Microsystems Engineering University Freiburg Germany Research Group for Modelling and Simulation of Mechatronic Systems Jade University of Applied Sciences Germany

ISBN: (纸本)9783800762040

We present a cantilever-free magnetic actuator, that allows for multistable vertical positioning within a movement range of several mm. Compared to classical solenoid actuation, it features three predefined stable positions in which no energy is consumed. We optimised the shape of the magnetic field to define these stable positions. In a proof of concept, our actuator achieved a response time down to 3.6 ms for the total displacement of 3.3 mm. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Magnetic actuators

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