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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...

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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 ...

引用

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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Optimized Energy Extraction in Tidal Current Technology using Evolutionary Algorithm 3

Optimized Energy Extraction in Tidal Current Technology usin...

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

作者： Tan, Min Keng Loo, Chun Chong Lim, Kit Guan Ibrahim, Pungut Goh, Hui Hwang Teo, Kenneth Tze Kin Modelling Simulation Computing Laboratory Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia School of Electrical Engineering Guangxi University Nanning China

ISBN: (纸本)9781665428996

Renewable energy is gaining more popularity recently. Tidal currents are driven by two different connected bodies trying to equalize their level differences, hence there will be a flow of water from the high-pressure head to the low-pressure head. It is this kind of water flow that makes tidal current suitable for power generation. The main advantage of tidal power is that it can be forecasted easily. Aside from that, sea water has higher density as compared to air, therefore for the same amount of power, the power can be generated at a lower speed. The tidal current model is composed of a permanent magnet synchronous generator, tidal velocity profile, and another two sub-systems. This model is simulated in Matlab. The resultant tidal velocity is made up of 5 different partial tides. The tidal current turbine model is tested with different inputs of pitch angle and tidal current speed. The results show that the maximum generated output power is 295kW when the pitch angle is 2.77°. Furthermore, the higher the tidal current speed, the higher the generated output power. Aside from that, as the pitch angle is gradually increased while keeping the tidal speed constant, the power coefficient will decrease. Maximum Power Point Tracking algorithm which is based on Perturb and Observe (PO) is used to locate the maximum power coefficient of the system. It can track the maximum power coefficient successfully but there will be oscillation at the steady state. Cuckoo Search via Levy Flight is able to overcome this problem as there will be no oscillation at steady state and this can prevent power loss. The convergence of Cuckoo Search via Levy Flight is two times faster than PO. © 2021 IEEE.

关键词： Flow of water

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Local Traffic Network Formulation and Signalisation via Benchmark Webster Model 4

Local Traffic Network Formulation and Signalisation via Benc...

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

作者： Ee Chuo, Helen Sin Tan, Min Keng Lim, Kit Guan Angeline, Lorita Wang, Tienlei Kin Teo, Kenneth Tze Modelling Simulation & Computing Laboratory Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia School of Computer Science Faculty of Innovation & Technology Taylor’s University Subang Jaya Malaysia Department of Intelligent Manufacturing Wuyi University Jiangmen China

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Traffic models have been widely studied for general traffic understanding and transportation variable relations, but unreadily exert for real time transportation decision and planning. With the present advancement in computer technology, the used-to-be-lengthy and complicated modelling technique is now more computable and executable than ever for putting into good use. This paper discusses extensively on the formulation of local traffic network in Kota Kinabalu, Malaysia and the signal settings as referred to the Jabatan Kerja Raya (JKR) using benchmark Webster Model. This paper is crucial for bridging the current traffic network modelling towards advanced modelling techniques and transportation network optimisation strategies to be incorporated in intelligent transportation systems. The traffic responses and results from the Webster model simulation and signalization were reported in this paper as a realistic reference under different case studies including normal traffic and congested traffic scenarios, besides when traffic delays occurred. © 2022 IEEE.

关键词： Traffic signals

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Minimizing Energy Expenditures in Multi-Hop Transmissions with Genetic Algorithm

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

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

作者： Kit Guan Lim Qi Yang Chin Min Keng Tan Ali Farzamnia Longxin Wei Kenneth Tze Kin Teo Modelling Simulation & Computing Laboratory Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia Faculty of The Internet of Things and Artificial Intelligence Fujian Polytechnic of Information Technology Fuzhou China

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.

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

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

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

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

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

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.

关键词： Decision support systems Weight measurement Accuracy Structure from motion Heuristic algorithms Estimation Fish Biomass Aquaculture Sensor arrays

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Distance Recognition Using QR Code Marker

Distance Recognition Using QR Code Marker

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

作者： Ian Jian Yi Lee Hou Pin Yoong Kenneth Tze Kin Teo Chang Yii Chai Biomechatronic Research Laboratory Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia High Voltage Power Energy Research Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Sabah

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

Existing distance measuring technologies such as ultrasonic distance sensor and laser range finders are commercially viable but have considerable drawbacks together with significant advantages. There is no balanced distancing solution when factoring together cost, size, maximum range and minimum clearance. This study proposes an incremental distancing solution which could minimize the individual drawbacks of the current absolute distancing solutions while maintaining most of their advantages. The solution consists of a device with a camera and processor that is capable of capturing two distance markers in a single frame and decode the encoded information in the distance markers. This solution will provide measurement precision more than each individual marker can provide itself based on the resolution of the camera and also the length between the two markers in frame. QR codes will be used as the distance marker due to their widespread use, error correction and royalty-free standard. The distance markers will have to be placed at a fixed distance interval onto a measuring surface. Millimeter precision is possible based on the calculations performed and verified with experimental results.

关键词： Costs Ultrasonic variables measurement Current measurement Lasers QR codes Length measurement Cameras Error correction codes Artificial intelligence Standards

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Evolutionary Based Control and Optimisation of Exothermic Batch Process

Evolutionary Based Control and Optimisation of Exothermic Ba...

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

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

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.

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Engine Fault Diagnosis using Probabilistic Neural Network 3

Engine Fault Diagnosis using Probabilistic Neural Network

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

作者： Zhu, Sheng Tan, Min Keng Chin, Renee Ka Yin Chua, Bih Lii Hao, Xiaoxi Teo, Kenneth Tze Kin College of Engineering Tongren Polytechnic College Tong Ren China 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

Engine failure is one of the major factors caused vehicle breakdown. In the current practice, the engine faults are diagnosed manually by mechanics and the accuracy is highly relied on their experience. Therefore, this study would like to explore the feasibility of implementing auto fault diagnosis using Probabilistic Neural Network (PNN). A benchmarked engine fault model is developed and simulated in Maltab. The proposed algorithm is designed to detect 9 common engine faults based on the information extracted from exhaust gas, such as hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx), carbon dioxide (CO2) and dioxygen (O2). The proposed PNN is trained using the collected engine fault data from experiment and the probability density of PNN is determined based on the Parzen window estimation method. Bayes decision rule is implemented for classifying the types of the engine faults. The simulated results show that the proposed algorithm has faster diagnosis speed, higher accuracy and consistent. The algorithm takes 0.038 s in diagnosing the fault and the average accuracy is 98.3 %. © 2021 IEEE.

关键词： Failure analysis

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