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International eConference on Computer and Knowledge engineering (ICCKE)

作者： Parisa Fard Moshiri Mohammad Nabati Reza Shahbazian Seyed Ali Ghorashi Cognitive Telecommunication Research Group Shahid Beheshti University G. C. Tehran Iran Faculty of Technology and Engineering Standard Research Institute Alborz Iran School of Architecture Computing and Engineering University of East London London UK

ISBN: (纸本)9781665402095

Human activity recognition (HAR) as an emerging technology can have undeniable impacts on several applications such as health monitoring, context-aware systems, transportation, robotics, and smart cities. Among the main research methods in HAR (sensor, image, and WiFi-based), the WiFi-based method has attracted considerable attention due to the ubiquity of WiFi devices. WiFi devices can be utilized to distinguish daily activities such as "walk", "run", and "sleep". These activities affect WiFi signal propagation and can be further used to recognize activities. This paper proposes a Deep Learning method for HAR tasks using channel state information (CSI). A new model is developed in which CSI data are converted to grayscale images. These images are then fed into a 2D-Convolutional Neural Network (CNN) for activity classification. We take advantage of CNN's high accuracy on image classification along with WiFi-based ubiquity. The experimental results demonstrate that our proposed approach achieves acceptable performance in HAR tasks.

关键词： Performance evaluation Smart cities Transportation Activity recognition Robot sensing systems Convolutional neural networks Task analysis

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Experimental Investigation of Curved and Straight Laminated Bamboo-Concrete Beams with New Calibration Method for Interface Slip

Research Square

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Research Square 2023年

作者： Deng, Yu Hao, Yuxi Mohamed, Ahmed Wong, Simon H.F. Tang, Yunchao Yuen, Terry Y.P. Sukontasukkul, Piti Shen, Minhe Fernando, Nirodha Saint, Ruth Zhang, Hexin School of Civil Engineering and Architecture Guangxi University of Science and Technology Liuzhou545006 China School of Computing Engineering and the Built Environment Edinburgh Napier University 10 Colinton Road EdinburghEH10 5DT United Kingdom Faculty of Science and Technology Technological Higher Education Institute of Hong Kong 20A Tsing Yi Road Tsing Yi Island New Territories Hong Kong College of Urban Construction Zhongkai University of Agriculture and Engineering Guangzhou510225 China Department of Civil Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Construction and Building Materials Research Center Department of Civil Engineering King Mongkut's University of Technology North Bangkok Thailand

This study echoes the rising demand for bio-based material in concrete composite structures in the race to accelerate carbon neutrality in construction. Noticing that most previous studies are focused on straight timber or engineered bamboo-to-concrete composite beams, this study focuses on: 1) developing unique and practical curved laminated bamboo-concrete (LBC) composite beams;2) experimentally investigating and comparing the structural performances of the curved and straight LBC composite beams;3) developing a novel and proper in-member interface shear slip calibration method that captures the longitudinal after-slip strain redistribution, validated by stain gauge measurements. This study also highlights the interlayer shear bonding strength of laminated bamboo as the thresholding parameter that determines the composite beams' overall flexural strength, evidenced by detailed failure mode analysis. The proposed interface shear slip calibration method can be extended to the other types of shear connectors such as screws, shear plates, etc. © 2023, CC BY.

关键词： Timber

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A differential public PUF design for lightweight authentication 16

A differential public PUF design for lightweight authenticat...

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2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Duan, Shengyu Sai, Gaole School of Computer Engineering and Science Shanghai University Shanghai200444 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (纸本)9783800755899

Physical Unclonable Functions (PUFs) have emerged as a promising primitive to provide a hardware keyless security mechanism for integrated circuit applications. Public PUFs (PPUFs) address the crucial PUF vulnerability of requiring databases to store the secrete reference information. This paper investigates one of the PPUF families, differential PPUF (dPPUF), which exploits complexly interacted logic to create time gap between actual execution and simulation, and to prevent simulation attacks. Conventional dPPUFs have high costs. We observe the repressers have large impact on simulation time, but take a great proportion in current dPPUF structure. We thereby present a represser network with specially designed interconnection and shared logic, to maintain a high circuit complexity and to reduce the area cost. We show the proposed dPPUF has similar resilience over simulation attacks as the conventional design. Area cost can be reduced by up to 44%, and thus the proposed design can be used for lightweight authentication. © VDE VERLAG GMBH.

关键词： Authentication

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Experimental investigation of mechanically laminated straight or curved-and-tapered bamboo-concrete T-beams

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engineering Structures 2023年 283卷

作者： Deng, Yu Hao, Yuxi Mohamed, Ahmed Wong, Simon H.F. Tang, Yunchao Yuen, Terry Y.P. Sukontasukkul, Piti Shen, Minhe Fernando, Nirodha Saint, Ruth Zhang, Hexin School of Civil Engineering and Architecture Guangxi University of Science and Technology Liuzhou545006 China School of Computing Engineering and the Built Environment Edinburgh Napier University 10 Colinton Road Scotland EdinburghEH10 5DT United Kingdom Faculty of Science and Technology Technological Higher Education Institute of Hong Kong 20A Tsing Yi Road Tsing Yi Island New Territories Hong Kong College of Urban Construction Zhongkai University of Agriculture and Engineering Guangzhou510225 China Department of Civil Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Construction and Building Materials Research Center Department of Civil Engineering King Mongkut's University of Technology North Bangkok Thailand

This study echoes the rising demand for bio-based material in concrete composite structures in the race to accelerate carbon neutrality in construction. Noticing that most previous studies are focused on straight timber or engineered bamboo-to-concrete composite beams, this study developed straight or curved-and-tapered mechanically laminated bamboo-concrete (LBC) T-beams. Six layers of 26 mm thick laminated bamboo panels were glue laminated together to form the bamboo beams. The curved bamboo beams have three different rises of arch: 50 mm, 100 mm and 150 mm. All specimen beams were tested by four-point bending tests to evaluate their structural performances of the curved and straight LBC T-beams. To monitor the flange-to-web interface shear transfer, a novel interface shear slip calibration method that captures the longitudinal after-slip strain redistribution was developed and validated by strain gauge measurements. This study also highlights the interlayer shear bonding strength of laminated bamboo as the thresholding parameter that determines the composite beams' overall flexural strength, evidenced by detailed failure mode analysis. The proposed interface shear slip calibration method can be extended to the other types of shear connectors such as screws, nails, shear plates and notched connections. © 2023 The Author(s)

关键词： Laminated composites

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Preliminary study on the spread of air-borne pollutants in urban environment: a CFD simulation approach

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Scientific Reports 2025年第1期15卷 1-19页

作者： Ahmad, Fatma Majumder, Debjit Ranjit, Rabs Gupta, Aman Manhart, Michael Chair of Computing in Civil Engineering and Geoinformation Systems and Geodetic Institute RWTH Aachen University Aachen Germany School of Engineering and Design Technical University of Munich Munich Germany Institute of Geography School of Geosciences University of Edinburgh Edinburgh United Kingdom Department of Architecture and Planning Indian Institute of Engineering Science and Technology Shibpur India

The spreading of pollutants within urban areas, particularly from traffic emissions, poses a significant health risk. Computational Fluid Dynamics (CFD) has emerged as a key tool in understanding how pollutants spread within a city. In particular, the large-eddy simulation (LES) approach allows us to capture the complex time-dependent behaviour of the 3D flow field due to buildings in a dense urban environment. This work utilizes the CFD tool MGLET (Multi Grid Large Eddy Turbulence) to model the transport of pollutants within a selected domain in Munich city. MGLET offers a feature of simulating transport of multiple passive scalar quantities simultaneously. This facilitates the individual analysis of emissions from each major street in the domain of interest, providing detailed insights into their respective impacts. Additionally, MGLET utilizes the Immersed Boundary Method to resolve 3D building geometries, removing the need to generate body-fitted grids, which tends to be highly time-consuming. The streets are defined by area sources, and the emission rates for each street are defined by the average traffic flow rate. This high-fidelity approach offers a detailed analysis, allowing us to identify local features such as recirculation zones in street canyons and pinpoint the streets that contribute most significantly to pollution at specific locations. The insights from CFD studies can empower policymakers to craft legislation tailored to local pollution control efforts, thus enhancing the quality of life in urban areas. Ultimately, the accurate prediction of pollutant concentration is critical, as it directly impacts the health and well-being of urban residents, highlighting the urgent need for effective pollution control measures. © The Author(s) 2025.

关键词： Air quality Computational fluid dynamics Large eddy simulation Wind simulation

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yan Zeng ChengChuang Huang YiJie Ni ChunBao Zhou JiLin Zhang Jue Wang MingYao Zhou MeiTing Xue YunQuan Zhang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou China Data Security Governance Zhejiang Engineering Research Center Hangzhou China School of ITMO Joint Institute Hangzhou Dianzi University Hangzhou China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing China HuaWei State Key Laboratory of Computer Architecture Institute of Computing Technology of the Chinese Academy of Sciences Beijing China

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network’s dataflow graph and device cluster’s topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method.

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NLP for Sentiment Analysis in Social Media Posts to Detect Suspicious Behaviour

NLP for Sentiment Analysis in Social Media Posts to Detect S...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Abhishek Awasthi Mohammad Bdair A. Nandha Kumar Sanjog Thapa Nalini Devi S Bharathi Ramesh Kumar Software Developer Sporta Technology pvt Ltd Maharashtra India School of Architecture Computing & Engineering University of East London London United Kindom Department of EEE Bannari Amman Institute of Technology Sathyamangalam India Department of Information System and Analysis Lamar University Texas USA Department of Management Studies St. Joseph's Institute of Technology Chennai India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In an era where social media is an almost omnipresent means of communication, online safety is crucial. Still, conventional sentiment analysis methods sometimes fall short of accurately spotting dubious conduct. A novel approach utilizing advanced Natural Language Processing (NLP) techniques, especially Deep Learning (DL) models like BERT (Bidirectional Encoder Representations from Transformers), is proposed to overcome these challenges. DL is used by the suggested method to identify nuanced attitudes in social media posts, which raises the accuracy of identifying suspicious activities. That is not the case with conventional systems that depend on basic algorithms. With detection rates of 88%, 85%, and 90% for hate speech, violent statements, and extreme opinions, respectively, the system accomplishes remarkable outcomes via meticulous data gathering, preprocessing, and model training. Through comparison, it outperforms existing systems in terms of F1 score (0.92), recall (0.91), precision (0.94), and accuracy (0.92). The proposed system effectively reduces potential risks and encourages a safer online environment by utilizing the effective analysis of sentiment in social media posts, therefore indicating a major advancement in online security.

关键词： Training Sentiment analysis Accuracy Social networking (online) Hate speech Bidirectional control Transformers Encoding Data models Security

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Circularity-based embodied carbon performance in building design: Index development and circular initiatives

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Journal of Cleaner Production 2025年 518卷

作者： Mohsen Ahmadi Asal Pournaghshband Farzad Piadeh M. Reza Hosseini School of Architecture and Built Environment Deakin University Geelong VIC 3220 Australia Centre for Engineering Research School of Physics Engineering and Computer Science University of Hertfordshire Hatfield AL10 9AB UK Smart Infrastructure and Green Technologies Research Group School of Computing and Engineering University of West London London W5 5RF UK Faculty of Architecture Building and Planning The University of Melbourne Parkville VIC 3010 Australia

This study introduces the Circularity-based Embodied Carbon (CiBEC) index, a comparative metric designed to assess the effects of circular actions on the embodied carbon (EC) of building projects. Built upon the conventional EC measurement, this index enhances traditional approaches by incorporating inclusive material inflow and outflow allocations, acknowledging variations in usage intensity, and factoring in potential changes to building lifespan strategies. Unlike conventional methods, it also recognises the collective efforts of all stakeholders - including third-party contributors - by fairly redistributing carbon savings across product (A) and beyond the building (D) phases of the life cycle assessment, offering a more holistic and equitable perspective. While demonstrated on a global scale, the index is applied to a real office building project to validate its practicality and demonstrate real-world applicability. Both individual and combined circular scenarios are assessed, revealing how key benefits from circular initiatives - often unaccounted for in conventional methods - are effectively captured through the CiBEC framework. The results indicate that among the eight developed circular scenarios, two - specifically, renovation and the multi-use of building spaces - are identified as EC-intensive. In contrast, the remaining scenarios achieve reductions of up to only 10 % when the conventional approach is used. However, when assessed using the CiBEC method, the “renovation” scenario notably demonstrates a 36 % reduction. Furthermore, the study shows that integrating various circular strategies through the development of seven combined scenarios can lead to substantial EC reductions, decreasing the initial EC from 629 to 191 kgCO 2 eq./m 2 . This significant reduction is not achieved through a simple statistical aggregation but through the complex interactions among the different strategies. Therefore, it seems that while the CiBEC index is most effective dur

关键词： Building industry Building lifespan Circularity Greenhouse gas Usage intensity

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Cap-Eye-citor: A Machine Vision Inference Approach of Capacitor Detection for PCB Automatic Optical Inspection 7

Cap-Eye-citor: A Machine Vision Inference Approach of Capaci...

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7th IEEE International Conference on engineering Technologies and Applied Sciences, ICETAS 2020

作者： Susa, Julie Ann B. Mariquina, Erwin Tria, Meriam L. Adolfo, Cid Mathew Castro, Julius C. Technological Institute of the Philippines College of Engineering and Architecture Manila Philippines International Electronics and Technical Institute Department of Computer Engineering Alabang Philippines Eulogio"Amang"Rodriguez Institute of Science and Technology Department of Electronics Engineering Manila Philippines University of Gloucestershire School of Computing and Engineering United Kingdom Bohol Island State University Department of Computer Engineering Bohol Philippines

ISBN: (纸本)9780738105048

Circuit boards are one of the key elements of the electronics industry. Because of its demand for portable electronic goods, the production of circuit boards has become more significant. The most significant aspect of printed circuit board production is AOI or Automatic Optical Inspection. When generated in a single quantity, the PCB requires a small way to teach and change the AOI method for process validation. This paper proposes a mechanism of detection of capacitors trained on circuit boards using the YOLO V3 algorithm. YOLO is a form of rapid object detection based on the convolutional neural network or CNN. CNN's deep network can distinguish specific characteristics from all the image features. The study developed an AI with the same feature that the manufacturing industry uses to assist students with an effective evaluation of their circuit boards. It focuses on the capacitor in a circuit board. It will not, however, be capable of distinguishing other forms of electronic components. Nearly 60 percent of the entire test has a performance of 100 percent detection, according to the test results, while 33.33 percent is over 70 percent detection and 6.66 percent has a 0 percent detection output. Capacitor recognition has been done with an overall testing accuracy of 93.33%. © 2020 IEEE.

关键词： Printed circuit boards

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Visual defect detection for historical building preservation

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Expert Systems with Applications 2025年 291卷

作者： Cheng, Mengqiu Zhang, Xinting Xia, Leihua Chen, Junming Xie, Jiayuan Ma, Zongfang Li, Qing School of Mechanical and Electrical Engineering Xi'an University of Architecture and Technology Xi'an China School of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an China Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Faculty of Humanities and Arts Macau University of Science and Technology Macau China Department of Mathematics The University of Hong Kong Hong Kong SAR China

Advancements in automated building defect detection technology are essential for preserving historical architecture. Although existing intelligent technologies have made notable progress in detecting defects in modern residential buildings, their application to historical buildings faces significant challenges due to the complexity of structures, material diversity, and long-term natural and human-induced deterioration. This study focuses on historical buildings in Guangzhou, China, and introduces a dedicated task for detecting defects in such buildings. To support this task, a high-quality, customized dataset has been developed as a research foundation. To address challenges such as image blurriness during data collection and the complex structural characteristics of historical buildings, this study proposes a baseline model named Deblur-DetNet. The model employs a two-stage approach that integrates image deblurring and defect detection, significantly enhancing both accuracy and robustness. Experimental results demonstrate that the proposed method performs exceptionally well in the task of historical building defect detection, particularly in identifying fine-grained defects. This research provides robust technical support for the protection and restoration of historical buildings and establishes a solid foundation for further studies in cultural heritage preservation. Our dataset is available at https://***/file/d/1-9NiAw0hzomWHOA9RMEMXOOwIq5HScPx/view?usp=sharing. © 2025

关键词： Computer vision Defect detection Historical building Image deblurring technology

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