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Journal of engineering Research (Kuwait) 2025年

作者： Abd Elgawad, Samar M. Hegazi, Yasmine Sabry Mohamed, Mady A.A. Elshanwany, Hussein Zamel, Amr A. Department of Architecture Faculty of Engineering Higher Technological Institute 10th of Ramadan City Egypt Department of Architecture Faculty of Engineering Zagazig University 44519 Egypt Department of Computer and Systems Engineering Department Faculty of Engineering Zagazig University Egypt

Not enough attention is paid to implementing energy efficiency measures for reusing heritage buildings in Egypt. This is due to a lack of awareness regarding preservation regulations and values. While these buildings were not initially designed for their current function, balancing preserving their heritage values and enhancing energy efficiency when implementing reuse requirements is crucial. Without such considerations, the retrofitted heritage buildings risk losing their identity, thus undermining the fundamental goal of preserving these structures for their continued existence. This study employed an experimental methodology approach, utilizing various techniques methods such as the theoretical method in addition to the mathematical method for data collection, the Delphi method for determining the significance of heritage value, the Pearl Building Rating system (PBRS) as target identification for material selection, and a genetic algorithm for selecting the optimal solution. The experimental methodology was formulated by combining all previous methods. To ensure the sustainable reuse of heritage buildings and preserve their cultural significance for future generations, the research aims to investigate the possibility of retrofitting heritage buildings while conserving their value and maximize energy efficiency through material selection. © 2025 The Authors

关键词： Adaptive reuse Energy Efficiency Genetic algorithm Heritage building Pearl building rating systems Retrofitting

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SpamVis: A Visual Interactive system for Spam Review Detection 28th

SpamVis: A Visual Interactive System for Spam Review Detecti...

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28th International Conference on Knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Lam, Nguyen Thanh Thao Le, Nu Uyen Phuong Islam, Md Rafiqul Sakib, Md KowsarHossain Prome, Shanjita Akter Sanin, Cesar Szczerbicki, Edward Zhou, Jianlong College of Business & Management VinUniversity Hanoi12426 Viet Nam Faculty of Engineering Architecture and Information Technology University of Queensland BrisbaneQLD4072 Australia Business Information Systems Australian Institute of Higher Education SydneyNSW Australia School of Computer Science Taylor's University Malaysia Faculty of Management and Economics Gdansk University of Technology Gdansk Poland School of Computer Science University of Technology Sydney Australia

In recent times, the number of spam reviews through various online platforms has emerged as a prime challenge, profoundly impacting businesses and consumers. These fake reviews not only distort clients' perceptions of products and services but also erode trust within the digital ecosystem. Despite the advent of machine learning (ML) techniques for identifying spam reviews, comparing text, and pinpointing groups of spammers, there remain notable gaps in both accuracy and the combination of interactive visualization for real-time decision-making. This paper presents SpamVis, a visual interactive system that leverages deep learning (DL) and ML blended with advanced visualization techniques for spam review detection, enabling analysts to conduct complicated analytical queries. The system allows users to input via click-on or touch to generate interactive charts and plots tailored for spam review analysis. The findings of the baseline test carried out on 67,395 review texts demonstrate that Bidirectional Encoder Representations from Transformers (BERT) carried out the best accuracy (86%) compared to other models. Our outcomes suggest that SpamVis can alleviate the gaps concerning accuracy and visualization needs in contemporary techniques, guiding analysts to make informed decisions for mitigating spam reviews and enhancing consumer trust. Furthermore, SpamVis empowers users to seamlessly discover the online reviews of various social media platforms in real-time, such as Facebook, Youtube, etc., giving them practical insights to navigate the online marketplace effectively. © 2024 The Authors.

关键词： Adversarial machine learning

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A robust multi-objective optimization algorithm for accurate parameter estimation for solar cell models

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Soft Computing 2024年第19期28卷 11265-11277页

作者： Singla, Manish Kumar Gupta, Jyoti Alsharif, Mohammed H. Kim, Mun-Kyeom Aljaidi, Mohammad Safaraliev, Murodbek Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering & ampTechnology Chitkara University Punjab India Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology K. R. Mangalam University Gurgaon Haryana122003 India Department of Electrical Engineering College of Electronics and Information Engineering Sejong University 209 Neungdong-Ro Gwangjin-Gu Seoul05006 Korea Republic of School of Energy System Engineering Chung-Ang University 84 Heukseok-Ro Dongjak-Gu Seoul06974 Korea Republic of Department of Computer Science Zarqa University Zarqa13110 Jordan Department of Automated Electrical Systems Ural Federal University Yekaterinburg620002 Russia

The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not provide the required information for reliable modeling. Consequently, accurate parameter estimation becomes necessary. This paper presents a simple multi-objective optimization algorithm (Hybrid Particle Swarm Optimization and Rat Search Algorithm (PSORSA)) designed to estimate cell parameters based on this observation. Unlike other optimization algorithms addressing this issue, the proposed algorithm aims to overcome challenges related to local minima and premature convergence, which often lead to suboptimal results. The paper focuses on assessing the reliability of the proposed algorithm by comparing its performance with other well-known optimization algorithms. The proposed optimizing algorithm is tested on the CEC 2019 benchmark function. Experimental results (RMSE), including statistical analysis, validate the algorithm's effectiveness by comparing them with other algorithms. At the end, non-parametric test is performed to justify the outcomes, vouching for the better performance of the proposed algorithm. The findings demonstrate that the proposed algorithms are particularly well-suited for estimating solar PV models. With its simple structure and high accuracy, the proposed algorithm exhibits great potential for various applications in the field of solar energy. Moreover, its computational efficiency and ease of implementation further contribute to its practicality. © The Author(s) 2024.

关键词： Solar cells

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VHF Band III Spectrum Availability and Utilization for Cognitive Radio Implementation in Malaysia 22

VHF Band III Spectrum Availability and Utilization for Cogni...

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22nd International Learning and Technology Conference, L and T 2025

作者： Elshafie, Hashim Mohamad, Hafizal Saeed, Rashid A. Hamdan, Mosab Khattab, Mahmoud M. Ahmed, Abdelmoty M. King Khalid University College of Computer Science 61421 Saudi Arabia Universiti Sains Islam Malaysia Faculty of Engineering and Built Environment Negeri Sembilan Nilai71800 Malaysia Taif21974 Saudi Arabia South East Technological University Walton Institute for Information and Communication Systems Science WaterfordX91 HE36 Ireland Al Yamamah University College of Engineering and Architecture Riyadh13541 Saudi Arabia Nahda University Faculty of Computer Science Beni Suef Egypt

ISBN: (纸本)9798331532215

The VHF Band III spectrum (174-230 MHz), recognized for its low utilization, was opened by the FCC in 2002 for opportunistic use by communication technologies. This paper presents a quantitative analysis of the availability of VHF Band III for cognitive radio CR access in Malaysia. Measurements were conducted over a week in 18 Malaysian cities to analyze spectrum utilization and received signal strength, alongside the impact of broadcasts from neighboring countries. The findings highlight actual utilization patterns and reveal the spectrum's potential for opportunistic access, especially post-digital switchover (DSO). © 2025 IEEE.

关键词： Cognitive radio

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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Model Predictive Control for B-Spline Trajectory Tracking in Omnidirectional Robots

Model Predictive Control for B-Spline Trajectory Tracking in...

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Iberian Robotics Conference (ROBOT)

作者： José Pedro Carvalho António Paulo Moreira A. Pedro Aguiar Faculty of Engineering University of Porto (FEUP) Porto Portugal SYSTEC-ARISE Research Center for System and Technologies Porto Portugal INESC TEC-Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

In the field of intelligent autonomous robots, integrating optimization techniques with classical control theory methods for mobile robot control is an increasingly prominent area of research. The combination enhances robots' ability to perform their tasks more efficiently, reliably, and safely. This paper addresses the development of a path and motion planning framework for omnidirectional robots, leveraging B-Splines and Trajectory Tracking with Model Predictive Control. The proposed framework is evaluated through software-in-the-loop tests using two distinct dynamical models and sets of hyperparameters. Final validation is conducted by implementing the framework within a ROS environment and performing field tests on a robotic platform. The results demonstrate that the robot can reliably track trajectories at its actuation limits, and the proposed framework enables the robot to increase its velocity up to 50% when compared to a PID path-following controller.

关键词： Uncertainty Trajectory tracking Wheels Predictive models Trajectory Planning Mobile robots Splines (mathematics) Robots Predictive control

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Discrete Event Simulation of Aircraft Sortie Generation on an Aircraft Carrier

Discrete Event Simulation of Aircraft Sortie Generation on a...

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2023 Winter Simulation Conference, WSC 2023

作者： Yoon, Hee Chang Oh, Seung Heon Woo, Jong Hun Chung, Jung-Hoon Lee, Hyuk Jung, Sun-Ah Seoul National University Department of Naval Architecture and Ocean Engineering 1th Gwanak-ro Gwanak-Gu Seoul08826 Korea Republic of Korea Institute of Machinery & Materials Mechanical Systems Safety Research Division Department of System Dynamics 156 Gajeongbuk-Ro Yuseong-Gu Daejeon34103 Korea Republic of

ISBN: (纸本)9798350369663

The Sortie Generation Rate (SGR) which refers to the number of sorties that can be generated per unit time, is a key indicator for evaluating the ability of an airbase. However, an aircraft carrier has many constraints compared to a land-based airbase, such as spatial and environmental constraints, making it difficult to apply existing land-based research to analyze aircraft carrier operations. On the other hand, the Sortie Generation Process (SGP) on an aircraft carrier is similar to a logistics/production system in that sorties are generated through aircraft. Therefore, this study proposes a framework for analyzing the SGP on an aircraft carrier using discrete event simulation and defines the classes that make up the simulation. In addition, SGP analysis simulations were implemented using the proposed framework and several experiments were performed to demonstrate the feasibility of applying the proposed framework in practice. © 2023 IEEE.

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A Vein Segmentation Method Based on Improved U-Net 12

A Vein Segmentation Method Based on Improved U-Net

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12th International Conference of Information and Communication Technology, ICTech 2023

作者： Wang, Hongliang Zhu, Liang Deng, Changyi Xiu, Pengfei Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China

ISBN: (纸本)9798350332919

Recently, medical research has revealed that diffusion weighted imaging (DWI) is less sensitive than susceptibility-weighted imaging (SWI) for acute ischemic stroke. Brain vein analysis in SWI is very important for personalised therapy, quantitative diagnosis, and prognosis of acute ischemic stroke. Therefore, the accurate segmentation of veins in SWI images has important significance in supplementary diagnosis and medical research. However, vein segmentation in SWI is a challenging task because different veins show distinct variability and uneven intensity and are difficult to manually split. This paper proposes an improvement method based on U-net, by using dense connection and mixing loss function, the best Dice coefficient and the ASSD were obtained by other comparison methods. The method presented in this article can be used as a potential tool to accurately measure SWI cerebral veins and help physicians in decision making. © 2023 IEEE.

关键词： Image segmentation

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Statistical static timing analysis for circuit aging prediction

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The Journal of China Universities of Posts and Telecommunications 2021年第2期28卷 14-23页

作者： Duan Shengyu Zhai Dongyao Lu Yue Shanghai Engineering Research Center of Intelligent Computing System Shanghai UniversityShanghai 200444China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Complementary metal oxide semiconductor(CMOS)aging mechanisms including bias temperature instability(BTI)pose growing concerns about circuit *** results in threshold voltage increases on CMOS transistors,causing delay shifts and timing violations on logic *** amount of degradation is dependent on the circuit workload,which increases the challenge for accurate BTI aging prediction at the design *** this paper,a BTI prediction method for logic circuits based on statistical static timing analysis(SSTA)is proposed,especially considering the correlation between circuit workload and BTI *** consists of a training phase,to discover the relationship between circuit scale and the required workload samples,and a prediction phase,to present the degradations under different workloads in Gaussian probability *** method can predict the distribution of degradations with negligible errors,and identify 50%more BTI-critical paths in an affordable time,compared with conventional methods.

关键词： bias temperature instability(BTI) reliability prediction statistical static timing analysis(SSTA)

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Assessment of Convolutional Neural Networks for Asset Detection in Dynamic Automation Construction Environments

Assessment of Convolutional Neural Networks for Asset Detect...

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2023 IEEE CHILEAN Conference on Electrical, Electronics engineering, Information and Communication Technologies, ChileCon 2023

作者： Guaman-Rivera, Robert Menendez, Oswaldo Arevalo-Ramirez, Tito Aro, Katherine Prado, Alvaro Garcia-Alvarado, Rodrigo Auat-Cheein, Fernando Institute of Engineering Sciences Universidad de o'Higgins Rancagua Chile Universidad Católica Del Norte Computer and Systems Engineering Antofagasta Chile Pontificia Universidad Católica de Chile Faculty of Engineering Santiago Chile Universidad Del Bio-Bio Department of Design and Theory of Architecture Concepción Chile School of Engineering and Physical Sciences Heriot-Watt University Edinburgh United Kingdom

ISBN: (纸本)9798350369533

Integrating social robotics into the construction industry, particularly in the context of Industry 5.0, faces several challenges in creating complex environments that seamlessly blend human and machine interactions. In this regard, the emergence of intelligent and expert systems holds promising technologies to enhance construction tasks focused on robots and workers in 3D printing applications. This work compares several methods of convolutional neural network-based object detectors designed to identify distinct construction assets and workers within the dynamic environment of 3D printing. To this end, different versions of the You Only Look Once v8 (YOLO v8) algorithm have been implemented, trained, and experimentally tested using several images captured within dynamic construction environments. Furthermore, we present an in-depth comparison between YOLO v8 and its preceding versions, namely YOLO v7 and YOLO v5. Experimental results disclosed the high performance of the proposed approach in effectively detecting three distinct entities (workers, robotic platforms, and building elements), achieving a precision rate of up to 98.8%. © 2023 IEEE.

关键词： Convolution

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