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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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Superacid assisted high-throughput production and solution-processing of pristine two-dimensional materials

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Materials Today 2025年 86卷 228-237页

作者： Deng, Qixuan Ma, Jiabin Liu, Minsu Zhou, Yicong Ding, Siyuan Zhan, Ke Zhu, Yuan Xiong, Zhiyuan Su, Yang Yu, Kuang Cheng, Hui-Ming Qiu, Ling Shenzhen Geim Graphene Center Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Laboratory for Simulation and Modelling of Particulate Systems Department of Chemical Engineering Department of Materials Science and Engineering Monash University VIC3800 Australia Monash Suzhou Research Institute Monash University Suzhou Suzhou SIP215000 China School of Microelectronics Southern University of Science and Technology Shenzhen518055 China School of Light Industry and Engineering South China University of Technology Guangzhou510640 China Shenzhen Key Lab of Energy Materials for Carbon Neutrality Shenzhen Institute of Advanced Technology Chinese Academy of Sciences 1068 Xueyuan Road Shenzhen518055 China Faculty of Materials Science and Energy Engineering Shenzhen University of Advanced Technology 291 Louming Road Shenzhen518107 China

Pristine two-dimensional (2D) materials have a multitude of interesting properties, promising a broad spectrum of applications. However, their poor affinity for common solvents and tendency to aggregate impede their effective exfoliation and solution-processing into various structures, such as fibers and membranes, thus limiting their practical use. We report a high-efficiency method for producing pristine 2D nanosheets by direct high-speed shearing exfoliation in trifluoromethanesulfonic acid (TfOH). Typically, this method produces hexagonal boron nitride nanosheets (BNNSs) with an unprecedented throughput of 38.8 mg mL-1h−1. The effective exfoliation is attributed to the strong binding affinity, large dipole moment and steric repulsion of TfOH, which collectively introduce layer edge distortion in hexagonal boron nitride (hBN), facilitating its exfoliation. The mechanical exfoliation and reversible edge interaction introduce no additional functional groups to the exfoliated BNNSs. The resulting pristine BNNS exhibit remarkable stability in TfOH solutions at ultrahigh concentrations of 200 mg mL−1 for over 28 days. The viscosity of the highly concentrated BNNS/TfOH dispersion can be adjusted, enabling techniques to produce BNNS-based spheres, fibers, and membranes with high thermal conductivity and mechanical strength, suitable for advanced thermal management applications. Room temperature processed BNNS-based spheres exhibit superior thermal conductivity compared to high temperature processed commercially available hBN or Al2O3 spheres. Moreover, TfOH can also be used to assist the efficient production and solution-processing of other 2D nanosheets, including graphene, molybdenum disulfide and tungsten disulfide, enabling the fabrication of high-quality bulk assemblies. © 2025 Elsevier Ltd

关键词： Molybdenum disulfide

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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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Selection of Charging Station Technology to Support the Adoption of Electric Vehicles in Indonesia with the AHP-TOPSIS Method 3

Selection of Charging Station Technology to Support the Adop...

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3rd International Conference on Power and Energy Applications, ICPEA 2020

作者： Setiawan, Andri D. Hidayatno, Akhmad Putra, Bramanda Dwi Rahman, Irvanu Universitas Indonesia Systems Engineering Modelling and Simulation Laboratory Industrial Engineering Department Depok Indonesia

ISBN: (纸本)9781728190297

The transportation sector is the largest oil energy-consuming sector and the second-largest emitter of gas emissions in Indonesia. To overcome this, the Government of Indonesia issued a presidential decree for electric cars in Indonesia, which targets to accelerate the adoption of electric vehicles in Indonesia. Several obstacles will be faced in adopting an electric vehicle in Indonesia, one of which is the readiness of an electric vehicle charging station. With a high investment value and a variety of charging technology forces the government to be able to choose the right technology. The objective of this research is to select the best alternative to provide recommendations for the Indonesian government in choosing the right type of charging station technology for Indonesia. In order to accomplish the aim, a hierarchy-based model has been developed by considering nine criteria and three alternatives, namely battery swapping, inductive, and conductive. This research uses the combined method of analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). Analysis reveals that the alternative, 'Conductive Charging,' holds the first rank among all considered alternatives. © 2020 IEEE.

关键词： Analytic hierarchy process

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

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

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Intelligent Systems and Computer Vision (ISCV)

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

ISBN: (数字)9798350350180

ISBN: (纸本)9798350350197

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.

关键词： Emotion recognition Computer aided instruction Ethics Computer vision Machine learning algorithms Distance learning Video sequences

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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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Classification of skin lesions using morpho-granulometry features derived from color clusters

Classification of skin lesions using morpho-granulometry fea...

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International Conference on System Theory, Control, and Computing (ICSTCC)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics "Dunarea de Jos" University of Galati Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics The Modelling & Simulation Laboratory "Dunarea de Jos" University of Galati Galati Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

The skin lesion can be thought of as a biological system, so the morpho-granulometry of significant color clusters found in skin lesions is one of the elements that reproduce in a natural way the structure of the lesion, this novelty is highlighted in this study. Important features of skin lesions can be modulated by fusing neural networks (NN) and machine learning (ML). By choosing the nevus and melanoma classes, the primary goal was accomplished, and three databases were used to test the methodology. The characteristics based on morpho-granulometry allowed for the identification of microstructure within the images, which can be very helpful in characterizing the biological system. Based on random forest (RF) and extreme gradient boosting (XGboost) classifiers, this work aimed to improve the classification performance of important feature selection. The selected features from three free image databases with three NNs were classified. In a binary classification of nevus vs. melanoma, the results showed that the pattern recognition neural network (PRNN), according to the PH2 database, provided an accuracy of 0.923 and an F1-score of 0.876. The classification is interpretable if it is not validated. In our study, the best results were verified with a logistic regression (LR) classifier.

关键词： Accuracy Recurrent neural networks Image color analysis Melanoma Biological systems Artificial neural networks Feature extraction Skin Lesions Classification tree analysis

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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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粉末床熔融的多材料铺粉过程中粉末扩散的数值研究

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Acta Mechanica Sinica 2023年第1期39卷 23-29页

作者：王林李二垒周宗彦张百成余艾冰 ARC Hub for Computational Particle Technology Department of Chemical & Biological Engineering Monash University Clayton VIC 3800 Australia Delft University of Technology Stevinweg 1 2628 CN Delft The Netherlands Jiangxi Provincial Key Laboratory for Simulation and Modelling of Particulate Systems Jiangxi University of Science and Technology Nanchang 330013 China Beijing Advanced Innovation Center Materials Genome Engineering Advanced Material & Technology Institute University of Science and Technology Beijing Beijing 100083 China

粉末床熔融增材制造技术已应用于功能梯度材料的制造.文献中报道了一种材料成分沿垂直于粉末铺粉方向变化的新设计.基于此设计,本工作采用离散单元数值模拟研究了两种密度差异较大的金属粉末在铺粉过程中颗粒的偏析情况.结果发现,在铺... 详细信息

粉末床熔融增材制造技术已应用于功能梯度材料的制造.文献中报道了一种材料成分沿垂直于粉末铺粉方向变化的新设计.基于此设计,本工作采用离散单元数值模拟研究了两种密度差异较大的金属粉末在铺粉过程中颗粒的偏析情况.结果发现,在铺粉过程中,较大密度粉末(重颗粒)一侧的铺粉量小于较小密度粉末(轻颗粒)的一侧的铺粉量.这种现象表明重颗粒从粉末密度大的一侧向粉末密度较小的一侧扩散.这种扩散受铺粉速度影响较大,铺粉速度越快,扩散越明显.但粉末层厚度对扩散影响不大.结果还表明,重颗粒与轻颗粒间的粒径差异影响扩散较大,减小重颗粒的粒径可能是控制颗粒偏析的一种方案.

关键词：材料成分颗粒偏析功能梯度材料离散单元金属粉末密度差异增材制造技术扩散影响

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