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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Shumbayaonda, T.P. Kamanzi, J. Kahn, M.T.E. Cape Peninsula University of Technology Engineering and Built Environment Faculty Cape Town South Africa

ISBN: (纸本)9798331535162

Energy storage has gained significant interest in the last few years due to the falling prices of electrochemical storage technologies. South Africa is currently having a shortfall in generation capacity which results in the system operator having to implement load shedding to avoid the grid collapse. The country's consumption profiles show peak demand in the early evening times and the system operator runs fossil fueled generators to meet the *** research aims to investigate the techno economic feasibility of a utility sized electrochemical storage that is coupled to a Solar PV 2 MWp system to meet the peak demand and reduce dependence on fossil fueled generators. Literature review is used to determine the most suitable energy storage technology. PVSyst software and Homer software were used to run performance and energy yield simulations from the battery energy storage system. Results indicate that the levelized cost of electricity from the battery energy storage system is lower than the LCOE from the open cycle gas turbines. The LCOE for the open cycle gas turbines was determined from literature to be R7.07/kWh and that of the BESS was determined to be R1.92 /kWh. The findings can assist in the quicker uptake of the technology by the utility and other independent power producers to increase generation capacity and reduce the current generation deficit. Other economic opportunities of the storage facilities are highlighted as they can be used to support the grid without compromising their primary objective of supplying evening peak power. © 2025 IEEE.

关键词： Battery storage

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A hydrodynamic modelling of wave-activated bodies for wavelet marine regions

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Clean Energy 2024年第2期8卷 35-47页

作者： J.S.Peh Mohammed W.Muhieldeen N.H.Shuaib Mechanical Engineering Department Faculty of EngineeringTechnology&Built EnvironmentUCSI UniversityKuala Lumpur56000Malaysia MTC Engineer Sdn.Bhd Shah AlamSelangorMalaysia

Recently,most wave energy research has targeted sea areas with high power density under extreme wave conditions for *** trend often leads to higher energy costs for wave power ***,many marine areas with low power density,characterized by wavelet conditions and less destructive forces than extreme wave conditions,remain ***,this study aimed to propose a wave-activated body model for sea regions with wavelet *** wave-activated body design process encompassed site selection,parameter determination,geometry design,comparison and performance evaluation using the ANSYS®AQWA *** results indicated that the proposed device achieved the desired heave motion,with an amplitude range of 1.2 to-2.5 m,validating its potential for deployment in marine regions with wavelet ***,while the proposed design is optimized for wavelet conditions,it was found to have potential limitations in extreme wave *** observation emphasizes the challenge of formulating a generalized design suitable for both ***,it is pivotal for wave-activated body designs to be customized based on the specific ocean conditions they target,underscoring the need for specialized designs of wave energy converters that consider the unique wave characteristics of their deployment sites.

关键词： wave energy converter South China sea renewable and sustainable energy wavelet ANSYS AQWA hydrodynamic wave-activated bodies

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Deep Learning for Breast Cancer Detection from Mammograms Images

Deep Learning for Breast Cancer Detection from Mammograms Im...

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International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023

作者： Choy, Juen Jet Tiang, Sew Sun Lim, Wei Hong Wong, Chin Hong Mastaneh, Mokayef Chow, Li Sze Chong, Kim Soon Sun, Bo Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur56000 Malaysia

ISBN: (纸本)9789819984978

Mammograms has been widely used for early detection of breast cancer;however, radiologists are prone to error which could lead to unnecessary tests or missed treatment window. With the advancement of state-of-the-art CNN models, transfer learning has become a popular technique for CAD development. In this study, a total of six state-of-the-art models are used, namely VGG16, Inception V3, ResNet50, EfficientNet B3, EfficientNet V2M and ConvNext were used to classify mammograms from two public datasets, INbreast and CBIS-DDSM. Prior to training and validation, the datasets were preprocessed with artefact removal, median filter, CLAHE and ROI extraction. The best performance was achieved by the ConvNext model trained on INbreast dataset, with an accuracy, sensitivity, specificity, precision, f1-score and AUC of 0.81, 0.8, 0.82, 0.67, 0.73 and 0.88, respectively. The results indicate that the model has the potential to improve the accuracy of breast cancer detection and reduce the risk of missed diagnoses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Median filters

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Object Detection in Autonomous Vehicles: A Performance Analysis

Object Detection in Autonomous Vehicles: A Performance Analy...

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International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023

作者： Lim, Yuxiang Tiang, Sew Sun Lim, Wei Hong Wong, Chin Hong Mastaneh, Mokayef Chong, Kim Soon Sun, Bo Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur56000 Malaysia

ISBN: (纸本)9789819984978

Automotive manufacturers are investing in smart cars, driverless processes, and pre-collision technologies to deliver safe and fuel-efficient mobility solutions. This paper aims to develop an advanced object detection model which incorporate more sophisticated techniques or architectures to identify commonly found objects on roads using one-stage (Single-Shot Detector) and two-stage object detectors (Faster R-CNN). The models are trained using TensorFlow and the Open Image Dataset V7, with ResNet50 architecture as the backbone. The Faster R-CNN models outperformed the SSD models, achieving a mean average precision of 92.5% at IoU 0.50. This paper presents the theory, methodology, and performance results of the work, which focuses on five classes of objects commonly found on roads (bus, car, motorcycle, stop sign, truck). The detection model exhibited an overall good performance in terms of precision and recall in object classification and localization, as evaluated from TensorBoard. The average precision value is 0.925, indicating accurate object classification. The recall value is 0.727, demonstrating successful object localization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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The stability of natural and man-made unsaturated slopes influenced by different rainfall patterns

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environmental Science and Pollution Research 2024年 1-19页

作者： Mohd Taib, Aizat Law, Ming Fei Munirwan, Reza Pahlevi Md Dan, Mohd Firdaus Putra Jaya, Ramadhansyah Department of Civil Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Department of Civil Engineering Faculty of Engineering Universitas Syiah Kuala Darussalam Banda Aceh23111 Indonesia Sustainable Geostructure and Underground Exploration Faculty of Civil Engineering and Built Environment Universiti Tun Hussein Onn Malaysia Johor Batu Pahat86400 Malaysia Faculty of Civil Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Lebuhraya Tun Razak 26300 Pahang Kuantan Malaysia

Many slope failures have been recorded in Malaysia in recent times, particularly during the rainy season. An irregular rainfall is a change in rainfall patterns that is found to affect the slope stability with the infiltration of rainfall. The objective of this research is to investigate variations in the pattern of rainfall and their effect on the stability of natural and man-made unsaturated slopes in two separate site locations. Case A is located near a student’s hall in Universiti Kebangsaan Malaysia (UKM), and another is located at the main entrance of UKM. Both cases are simulated with current and projected rainfall. The behaviour of the slopes with applied rainfall is analyzed with generated pore-water pressure, displacement and factor of safety. The first case was conducted by including the rainfall records from January 2011 to January 2021 and future-oriented data in January 2050. The software package used for this case is Plaxis2D. The results indicate that the highest factor of safety captured for 2021 was 1.156, while the lowest factor of safety was 1.11. It is observed that the obtained safety factor decreased with increasing rainfall intensity. Moreover, the displacement obtained was 0.1761 m. In Case B, the capillary barrier system was used consisting of two layers of soil with different hydraulic properties as slope cover. A parametric study was performed to investigate the effect of several variables, including the hydraulic conductivity of soil and the rate of infiltration. By using SEEP/W from the GeoStudio, the results obtained show that the pore-water pressure in the initial slope is higher than the slope with the capillary barrier system. In addition, the factor of safety was calculated in SLOPE/W, indicating that the slope with a capillary barrier system has a high factor of safety than the original slope. The capillary barrier system was seen to improve slope stability by reducing the rainwater infiltration. These two cases were investigated

关键词： Safety factor

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Exploring the Combined Effects of Socio-Political Conflicts and Patriarchic Society on a Construction Career Path: Evidence from Afghan Female Professionals

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International Journal of Construction Education and Research 2024年第1期20卷 98-118页

作者： Kakar, Ahmad Shah Hasan, Abid Construction Engineering and Management Dept. of Civil Engineering Indian Institute of Technology Delhi New Delhi India School of Architecture and Built Environment Faculty of Science Engineering and Built Environment Deakin University Geelong Australia

Women’s under-representation in the construction industry is evident in developing and developed economies worldwide. Previous studies show that several factors, including cultural and societal norms, result in the low participation of women in the construction industry. This study explores the combined effects of socio-political conflicts and patriarchic society on female construction professionals’ experiences and career paths in the Afghan construction industry. The thematic analysis of the qualitative data collected from in-depth semi-structured interviews with eight Afghan female construction professionals revealed that social and political unrest influenced their career choices and career growth in the Afghan construction industry. In addition, widespread corruption and security issues distinctly shaped their work experiences. The findings also revealed the negative influence of overprotective culture and religious beliefs on women professionals’ experiences and careers. Finally, this study found that office-based roles, family support, and technology adoption encouraged women’s participation in the Afghan construction industry. Given the scarcity of research from similar contexts, this study provides empirical evidence on various challenges female professionals face in the construction industry in patriarchic societies and war-affected and conflict-sensitive regions. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： Afghanistan Construction industry patriarchy war women professionals

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3D Brain Tumour Segmentation Using UNet with Quantitative Analysis of the Tumour Features 1

3D Brain Tumour Segmentation Using UNet with Quantitative An...

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1st International Conference on Electronic and Computer engineering, ECE 2023

作者： Azeez, Ibrahim Izdhan Chow, Li Sze Solihin, Mahmud Iwan Ang, Chun Kit Department of Electrical and Electronic Engineering Faculty of Engineering Technology and Built Environment UCSI University Malaysia Department of Mechanical Engineering Faculty of Engineering Technology and Built Environment UCSI University Malaysia

Brain tumours are abnormal cells that grow within the brain and may cause severe harm to an individual. The diagnosis of tumours often requires a manual review of Magnetic Resonance (MR) images by a skilled radiologist, which is a laborious task. The study aims to use a deep learning-based approach to segment the brain tumour and quantitatively evaluate the performance of the model by comparing the statistical distribution of five textural and four non-textural feature differences between the ground truth and the prediction. Textural features were extracted from a Grey Level Co-occurrence Matrix (GLCM) and averaged over four different directions. A UNet model was constructed with MONAI using BraTS 2020 dataset. Due to hardware constraints, the model was trained over a short epoch range of 100 but managed to achieve a mean Dice similarity coefficient (DSC) of 0.86. The quantitative analysis showed that the ground truth and the prediction had low differences in most features, except for percentage variation in signal intensity, energy, correlation and Hausdorff distance, which could be attributed to false positive voxels that are far from the ground truth. The textural properties of homogeneity, entropy, and correlation had an average absolute percentage difference of less than 5% from the ground truth. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Tumors

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Enhancing Student Engagement Through Problem-Based Learning: A Case of the built environment 1

Enhancing Student Engagement Through Problem-Based Learning:...

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1st International Conference on Education Research, ICER 2024

作者： Nemakhavhani, Rotondwa Benevolence Faculty of Engineering Built Environment and Information Technology Central University of Technology Free State South Africa

ISBN: (纸本)9781917204262

In the rapidly evolving field of the built environment, traditional teaching approaches often fall short in preparing students for the complex, real-world challenges they will face in their professional careers. Problem-Based Learning (PBL) has emerged as a powerful pedagogical approach that promotes active learning and student engagement by encouraging learners to collaboratively solve real-life problems. This study explores the impact of PBL on student engagement within the built environment at the institutions of higher learning in particular the University of Technologies. The purpose of this research is to investigate how PBL can be effectively implemented to enhance active learning and foster deeper student engagement. This study adopted a qualitative research approach employing a systematic literature review (SLR) methodology, focusing on academic sources published between 2010 and 2024. The gathered data was synthesized using thematic analysis, wherein major themes and patterns were identified within the literature. Key themes such as collaborative learning, critical thinking, and real-world application were identified. The findings reveal that PBL not only increases student engagement but also enhances critical thinking and problem-solving skills, which are essential for success in the built environment sector. The study also highlights challenges, including the need for facilitator training and the development of appropriate assessment strategies to support the successful implementation of PBL. The implications of this study are significant for educators and administrators in higher education. By integrating PBL into the curriculum, the University of technology can better prepare its students for the demands of the modern workforce, fostering a learning environment that promotes both academic excellence and practical skills development. This research contributes to the growing body of literature on active learning in higher education and provides a framewo

关键词： Collaborative learning

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Power Line Loss Reduction Based on Meta-Heuristic Algorithm in Smart IoT environment

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第4期9卷 2216-2233页

作者： Du, Hui-Na Ge, Jun-Chao Hebi Institute of Engineering and Technology Henan Polytechnic University Hebi458030 China Faculty of Engineering Built Environment and Information Technology SEGi University Selangor Darul Ehsan Kota Damansara47810 Malaysia

The problem of power line loss reduction in an intelligent IoT environment is a very challenging one, but by using IoT technologies and smart algorithms, it is possi-ble to achieve power line loss reduction. High Temperature Superconductor (HTS) plays an important role in the power industry due to its near lossless transmission capability and efficient transmission efficiency. Aiming at the problem that uneven current distribution in each conductive layer of HTS cables will lead to an increase in AC losses, this paper proposes a method for optimising the structural parameters of HTS cables based on a meta-heuristic algorithm. Firstly, the general structure and characteristics of HTS are introduced. Secondly, the AC characteristics of HTS cables are investigated and the AC transmission loss is analysed with emphasis. Then, a function is established with the balanced distribution of currents in each layer as the optimisation objective, and an improved fruit fly optimisation method is used to optimise the structural parameters of the cable’s conductive layer. The constraints of the optimisation algorithm are the mechanical characteristics of the HTS cable and the critical current. The experimental results demonstrate the feasibility of the improved FOA algorithm. The structural parameter optimisation of HTS cables using the improved FOA can effectively reduce the AC losses, which can help to realise high-quality transmission and transformation systems over long distances and with high currents. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Power cables

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Reinforcement Learning for Smart Mobile Factory Operation in Linear Infrastructure projects 41

Reinforcement Learning for Smart Mobile Factory Operation in...

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41st International Symposium on Automation and Robotics in Construction, ISARC 2024

作者： Cao, Jianpeng Čustović, Irfan Soman, Ranjith Hall, Daniel Faculty of Architecture & the Built Environment Delft University of Technology Netherlands Faculty of Civil Engineering and Geosciences Delft University of Technology Netherlands

ISBN: (纸本)9780645832211

Mobile factories promise an increased project efficiency with on-demand production and Just-in-Time delivery of prefabricated elements. However, traditional scheduling methods predominantly focus on either factory or site and neglect the factory mobility, often leading to suboptimal synchronization. To address this gap, this paper introduces a novel reinforcement learning (RL)-based model for optimizing the operational policy of mobile factories in infrastructure projects. The developed model simultaneously schedules on-site and off-site operations, effectively integrating the performance metrics at the project level. Utilizing RL, the factory's production management system continuously learns and adjusts in response to real-time project developments, ensuring optimal decision-making regarding scheduling and resource allocation. © 2024 ISARC. All Rights Reserved.

关键词： Reinforcement learning

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