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Distance Recognition Using QR Code Marker 6

Distance Recognition Using QR Code Marker

引用

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

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

ISBN: (纸本)9798350389692

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. © 2024 IEEE.

关键词： Cameras

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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...

引用

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...

引用

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...

引用

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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Analyzing Recorded Video to Evaluate How Engaged and Emotional Students Are in Remote Learning Environments 6

Analyzing Recorded Video to Evaluate How Engaged and Emotion...

引用

6th International Conference on Intelligent Systems and Computer Vision, ISCV 2024

作者： Abdellaoui, Benyoussef Remaida, Ahmed Sabri, Zineb Abdellaoui, Mohammed El Bouzekri El Idrissi, Younes Moumen, Aniss Ibn Tofaïl University Laboratory of Engineering Sciences National School of Applied Sciences Kenitra Morocco Ibn Tofaïl University Laboratory of Computer Science Faculty of Sciences Kenitra Morocco Mohammed V University LPHE-Modelling and Simulation Faculty of Sciences Rabat Morocco

ISBN: (纸本)9798350350180

The prevalence of distance learning has led to an increasing concern regarding the dropout rate in educational environments. However, efforts are being made to enhance the quality of learning in academic institutions by changing learning platforms and teaching methods. Many unresolved problems exist, such as increasing school dropout rates and little attention to students' emotional state and health conditions. The proposed solution involves analyzing recorded student videos, which will provide valuable insights into student emotion and engagement and help to reduce the dropout rate. This paper proposes a new approach to assessing students' engagement and emotional states in distance learning environments without direct interaction. This study suggests a system for analyzing student-recorded video sequences in remote learning scenarios. The proposed approach combines advanced algorithms and machine learning methods to assess students' levels of engagement and identify their emotions. The study's findings show how an intelligent application may be created to assist educators in evaluating the feelings and engagement of their students. For instructors, this application is a valuable resource. It gives them insightful information on the experiences of their students and helps them adjust their teaching methods accordingly. © 2024 IEEE.

关键词： Video analysis

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Transmission path optimization Based on Efficiency Communication System

Transmission path optimization Based on Efficiency Communica...

引用

2022 International Symposium on Sensor Technology and Control, ISSTC 2022

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

Routing is the core of wireless sensor network deployment. This paper points out the research focus of the wireless sensor network transmission path and discusses the optimization problem of the transmission based on grid model. In this paper, routing attribute table that is maintained by the nodes, the density and relative distance of node distribution, is used to determine the clustering method. At the same time, the developed algorithm combines the relative position of the cluster and the base station to further improve the routing method of the sub-cluster head, Finally, the algorithm analyzes the influence of different parameters on the transmission path, and use the simulation experiments to evaluate the conclusions. © Published under licence by IOP Publishing Ltd.

关键词： Sensor nodes

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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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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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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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Multi-Area Load Frequency Control Using Particle Swarm Optimization

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

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

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

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.

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