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Innovative Research in Applied Science, engineering and Technology (IRASET),Conference on

作者： Sabrina Jellaj Salma Ouhaibi Laidi Zahiri Naoual Belouaggadia Laboratory of Modelling and Simulation of Intelligent Industrial Systems (M2S2I) ENSET Hassan II University of Casablanca Morocco Laboratory Engineering Systems and Applications ENSA Sidi Mohamed ben Abdellah University Fez Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

This research evaluates the effectiveness of two types of phase-change materials (PCMs): one paraffin-based (PCM1) and the other bio-based (PCM2), integrated within a multi-layer wall system. The thermal management and energy-saving capabilities of these MCPs were examined under diverse climatic conditions by numerical simulations performed with ANSYS 2021 R1. The results indicate that PCM1 is more efficient at storing latent heat, allowing it to maintain thermal conditions for extended durations, which makes it particularly suitable for regions experiencing constant temperature fluctuations. On the other hand, PCM2 reacts quickly to sudden temperature shifts because of its reduced melting point and enhanced thermal conductivity, optimizing performance across various environmental settings. Both PCMs markedly diminished indoor temperature fluctuations, achieving energy savings of roughly 18% for cooling requirements. Furthermore, PCM2's bio-based composition provides significant environmental benefits, consistent with sustainable construction methodologies. The results highlight the synergistic advantages of PCM1 and PCM2, stressing the necessity to customize PCM selection according to particular climatic and operational demands to improve building energy efficiency and thermal comfort. This research advocates for the incorporation of phase change materials into wall systems as an effective strategy for sustainable architectural solutions.

关键词： Phase change materials Hands Fluctuations Buildings Energy conservation Conductivity Thermal management Energy efficiency Thermal stresses Thermal energy

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Machine learning-based multi-objective optimization of a carbon dioxide direct-expansion geothermal heat pump comprising an internal heat exchanger and expander

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Applied Thermal engineering 2025年 274卷

作者： Askari, Ighball Baniasad Ghazizade-Ahsaee, Hossein Shahsavar, Amin Keshtegar, Behrooz Department of Mechanical Engineering Faculty of Engineering University of Zabol Zabol Iran Tehran Iran Department of Mechanical Engineering Kermanshah University of Technology Kermanshah Iran Department of Civil Engineering Faculty of Engineering University of Zabol Zabol Iran Key Laboratory of Modelling and Simulation-based Reliability and Optimization University of Zabol ZabolP.B. *** Iran

The present study deals with the intelligent forecasting of the coefficient performance (COP), exergy efficiency (ηex), and capital cost of a transcritical CO2 direct expansion heat pump using a heuristic machine learning approach. For the first, the parametric analysis was conducted on six important variables to examine the influence of each on ηex, COP, and the length of ground heat exchanger (LGHX). The outputs were used to develop a predictive model using the Radial Basis Functions (RBFs) artificial numeral network machine learning approach. The results demonstrated that LGHX is significantly diminished by 70.21 % as the temperature difference between the soil and evaporator increases from 5 °C to 13 °C. Also, LGHX is reduced by 21.69 % as Tin,w is intensified. Moreover, an 80 % increase in Tin,w leads to 50.12 % and 45.55 % improvement in ηex and COP, respectively. In addition, the RBF machine learning approach exhibited a high performance and accuracy in forecasting ηex, COP, LGHX, heating energy (QGC), capital cost, and the compressor and expander work with R2 ≥ 0.9943 and a low RMSE. Moreover, the results of multi-objective genetic algorithm optimization showed that the heat pump with LGC, LGHX, and compressor speed of 10.06 m, 172.5 m, and 2158 RPM brings the optimum ηex and COP of 34.22 % and 3.05, respectively, delivering heating energy of 10.3 kW with a total capital cost of 22,303 $. © 2025 Elsevier Ltd

关键词： Geothermal heat pumps

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods

Detection of Detonators in Baggage Using Deep CNN Architectu...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Oulhissane Lynda Merah Mostefa Simona Moldovanu Luminita Moraru Laboratory of Signals and Systems LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Mostaganem Algeria MSlab Department of Computer Science and Information Technology Modelling & Simulation Laboratory Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania MSlab Department of Chemistry Physics & Environment Modelling & Simulation Laboratory Faculty of Sciences and Environment Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL

关键词： Wavelet transforms Radiography Visualization Gray-scale Inspection Feature extraction Libraries

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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 ...

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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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Improved Ductility by Reducing Powder Size in Laser Powder Bed Fusion of AlSi10Mg

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Additive Manufacturing Frontiers 2024年第1期3卷 160-172页

作者： Fuzhong Chu Haopeng Shen Jianwen Li Juan Hou Kai Zhang Zongyan Zhou Yuman Zhu Xinhua Wu Aijun Huang ARC Research Hub for Computational Particle Technology Department of Chemical and Biological EngineeringMonash UniversityClaytonVIC 3800Australia Monash Centre for Additive Manufacturing Monash UniversityNotting HillVIC 3168Australia Department of Materials Science and Engineering Monash UniversityClaytonVIC 3800Australia School of Materials and Chemistry University of Shanghai for Science and TechnologyShanghai200093China Jiangxi Provincial Key Laboratory for Simulation and Modelling of Particulate Systems Jiangxi University of Science and TechnologyNanchang330013China

Strict control of powder properties,especially particle size distribution(PSD),is critical in the laser powder bed fusion(LPBF)process to ensure the quality of the fabricated *** work shows that reducing the powder size could improve the ductility of LPBF fabricated *** tensile elongation increased from 4.1%to 8.8%when AlSi10Mg powder sizes(D50 value)decreased from 76μm to 16μm in the as-fabricated *** results showed that fine powder size stimulates epitaxial grain growth due to excessive *** presence of epitaxial columnar grains makes the crack path across inner melt pools more frequent and travels from the coarse melt pool zones to the fine melt pool *** work suggests that powder size is a crucial factor to be considered in maintaining repeatable mechanical properties in LPBF processes,particularly when ductility and ductility-dominated properties are critical.

关键词： Particle size distribution Laser powder bed fusion Ductility Epitaxial columnar grain

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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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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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Numerical study of a new ecological material in moroccan climate: CFD approach

Numerical study of a new ecological material in moroccan cli...

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Innovative Research in Applied Science, engineering and Technology (IRASET),Conference on

作者： Mohammed Fellah Salma Ouhaibi Naoual Belouaggadia Khalifa Mansouri Laidi Zahiri Laboratory of Modelling and Simulation of Intelligent Industrial Systems ENSET Hassan II University Mohammedia Morocco Laboratory Engineering Systems and Applications ENSA Sidi Mohamed ben Abdellah University Fez Morocco

The thermal insulation of buildings based on ecological materials resets the excellent solution to decrease the impact of inorganic materials on the environment, using materials of biological origins, such as Durian, Bagasse, Date Palm, Pineapple Leaves, Wood fibers, etc. With their low intrinsic energy, they can improve the quality of the environment and the thermal and energetic performance of the building; for this reason, a comparative study is made numerically using a method of computational fluid dynamics (CFD) two-dimensional between a wall without insulation and a wall with insulation based on an ecological coating based on Aerosil, in the Moroccan climate, and precisely in the city of Ifrane which characters by a cold climate, the solution presented efficient energy savings during both winter and summer season. The results showed that the configuration with the insulation generates an energy saving of 14% during the winter and 61.1% during the summer. The interior surface temperature was reduced by about 4 °C during summer days and increased by about 2 °C during winter. The effect of thickness on energy sheathing increased from 61.1% to 88.2% during summer and from 14% to 47% during winter.

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