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arXiv 2024年

作者： Sohail, Ammar Shen, Bojie Cheema, Muhammad Aamir Ali, Mohammed Eunus Ulhaq, Anwaar Babar, Muhammad Ali Qureshi, Asama Faculty of Information Technology Monash University Australia Department of Computer Science and Engineering BUET Bangladesh School of Engineering and Technology Central Queensland University Australia Centre for Research on Engineering Software Technologies University of Adelaide Australia

As urban areas grapple with unprecedented challenges stemming from population growth and climate change, the emergence of urban digital twins offers a promising solution. This paper presents a case study focusing on Sydney's urban digital twin, a virtual replica integrating diverse real-time and historical data, including weather, crime, emissions, and traffic. Through advanced visualization and data analysis techniques, the study explores some applications of this digital twin in urban sustainability, such as spatial ranking of suburbs and automatic identification of correlations between variables. Additionally, the research delves into predictive modeling, employing machine learning to forecast traffic crash risks using environmental data, showcasing the potential for proactive interventions. The contributions of this work lie in the comprehensive exploration of a city-scale digital twin for sustainable urban planning, offering a multifaceted approach to data-driven decision-making. © 2024, CC BY.

关键词： Risk analysis

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Materials Behaviour Analysis of 3D Printed Brass-PLA Filament 7

Materials Behaviour Analysis of 3D Printed Brass-PLA Filamen...

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7th International Conference on Mechanical engineering Research 2023, ICMER 2023

作者： Rajan, Dinesh Samykano, M. Suraparaju, S.K. Moorthy, K. Kadirgama, K. Ramasamy, D. Pandey, A.K. Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang26600 Malaysia Centre for Research in Advanced Fluid and Process University Malaysia Pahang Al-Sultan Abdullah Lebuhraya Tun Razak Pahang Kuantan Gambang26300 Malaysia Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Al-Sultan Abdullah Kuantan Malaysia School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Petaling Jaya47500 Malaysia

The current study aims to bridge a crucial gap in existing research, potentially paving the way for a groundbreaking transformation in the development and application of PLA/Brass composites within diverse industries such as aerospace, automotive, consumer goods, and medical devices. The primary objective of this research is to assess the mechanical properties of a composite material made up of Polylactic Acid (PLA) and Brass, produced using Fused Deposition Modelling (FDM) 3D printing technology. Brass, renowned for its exceptional mechanical properties, has been integrated into PLA to form this composite material. The study employs various analytical techniques, including Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and Energy-Dispersive X-ray Spectroscopy (EDX), to scrutinize the chemical and physical characteristics of the PLA/Brass composite. This research revolves around exploring the impact of different printing parameters on the mechanical behavior of the printed specimens. The investigation delves into aspects such as tensile strength, compression resistance, bending properties, and impact resistance. To achieve this, test specimens with varying compositions have been produced using a Raise3D N2 Plus FDM 3D printer, with careful manipulation of printing parameters such as layer height and printing speed. The compositional variations range from 15% wt. to 80% wt., with layer height values spanning 0.25 mm, 0.30 mm, and 0.35 mm, and printing speeds ranging from 20 mm/s to 40 mm/s. The outcomes of this research have revealed the distinct influences of specific printing parameters on various mechanical properties. For example, in the context of tensile testing, it was observed that the combination of a layer height of 0.25 mm and a printing speed of 30 mm/s resulted in the highest elastic modulus. Similarly, the study provides crucial insights into optimizing PLA/Brass composite material properties through controlled addit

关键词： Scanning electron microscopy

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Locally differentially private distributed algorithms for set intersection and union

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Science China(Information Sciences) 2021年第11期64卷 234-236页

作者： Qiao XUE Youwen ZHU Jian WANG Xingxin LI Ji ZHANG College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization Faculty of Health Engineering and Science University of Southern Queensland

Dear editor,Privacy-preserving distributed set intersection and union(PPSI, PPSU) have received much attention in recent years because of their wide applications. Most of existing solutions [1, 2] utilize secure multiparty computation protocols(SMCP) [3, 4] to settle the problem, but the SMCP methods are expensive in computation and communication. Even worse, most SMCP methods hardly continue to work if some participants disconnect.

关键词： local differential privacy set intersection union randomized response multiset

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PASTD: Progressive Augmentation and Spatiotemporal Decoupling Contrastive Learning for Skeleton-Based Action Recognition

PASTD: Progressive Augmentation and Spatiotemporal Decouplin...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qian Huang Weiwen Qian Chang Li Gongyou Xu Zhongqi Chen College of Computer Science and Software Engineering Hohai University Nanjing China Faculty of Science and Technology United International College BNU-HKBU Zhuhai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Contrastive learning has achieved significant progress in the field of self-supervised skeleton-based action recognition. However, existing methods often apply strong augmentations directly to skeleton data, which can distort or even lose the semantic of the skeletons. Additionally, most methods focus on unified feature extraction through spatiotemporal modeling, leading to spatiotemporal entanglement that hinders model’s interpretability and degrades performance. To address these issues, we propose a Progressive Augmentation and SpatioTemporal Decoupling contrastive learning model (PASTD). Specifically, we propose progressive augmentation to enhance the model’s generalization capability, which generates multiple distinct positive sample pairs through multiple branches and uses inter-branch constraints to maintain consistent motion patterns. Moreover, we propose spatiotemporal decoupling module to separate the feature’s spatial and temporal information, using a dual-path self-attention module combined with an intra-branch cross-domain learning strategy to facilitate information exchange between domains. Extensive experiments on the four public datasets NTU-RGB+D (60&120) and PKU-MMD (I&II) demonstrate the effectiveness of these components. Moreover, PASTD outperforms state-of-the-art methods across various evaluation metrics.

关键词： Measurement Three-dimensional displays Semantics Contrastive learning Speech recognition Signal processing Skeleton Spatiotemporal phenomena Speech processing Information exchange

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Architecting Digital Twins for Intelligent Transportation Systems

Architecting Digital Twins for Intelligent Transportation Sy...

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IEEE International Conference on software Architecture Workshops (ICSAW)

作者： Hiya Bhatt Sahil Karthik Vaidhyanathan Rahul Biju Deepak Gangadharan Ramona Trestian Purav Shah Software Engineering Research Centre IIIT Hyderabad India Computer Systems Group IIIT Hyderabad India Faculty of Science and Technology Middlesex University London

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation Systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.

关键词： Adaptation models software architecture Transportation computer architecture Predictive models Traffic control Real-time systems Digital twins Safety Predictive analytics

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PyTime IoT: A Bootcamp to Motivate High School Students to Choose STEM Careers

PyTime IoT: A Bootcamp to Motivate High School Students to C...

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IEEE Education engineering (EDUCON)

作者： Adriana Collaguazo Monica Villavicencio Alain Abran Carmen Vaca-Ruiz Faculty of Electrical and Computer Engineering FIEC Escuela Superior Politécnica del Litoral ESPOL Guayaquil Ecuador Department of Software and IT Engineering École de Technologie Supérieure (ETS) Montreal Canadá

ISBN: (数字)9798331539498

ISBN: (纸本)9798331539504

In the rapidly evolving landscape of modern education, Science, Technology, engineering and Mathematics (STEM) disciplines stand out for their role in equipping students with the skills necessary to address complex real-world problems. Despite the critical importance of STEM careers, disparities persist, particularly among young women and students from public schools. This motivated the creation of an IoT bootcamp - referred to as PyTime IoT, a 2-day course aimed at high school students. This bootcamp integrates hands-on activities in Python programming and IoT systems that demonstrate the tangible application of theoretical STEM concepts through real-world scenarios. Our approach enhances participants' understanding of IoT and programming and also serves as a stepping-stone for high school students contemplating STEM careers. Preliminary results from the first two bootcamp editions indicate a positive shift in participants' skills and a marked increase in STEM career interest, suggesting that targeted bootcamps like PyTime IoT are effective in bridging the educational gap and inspiring the next generation of STEM professionals.

关键词： Industries engineering profession Data handling Organizations Programming profession Next generation networking Monitoring STEM Python Gender issues

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Urine dataset having eight particles classes

arXiv

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arXiv 2023年

作者： Tuncer, Taner Erkuş, Merve Çınar, Ahmet Ayyıldız, Hakan Tuncer, Seda Arslan Fırat University Faculty of Engineering Computer Engineering Elazığ23119 Turkey Elazığ Fethi Sekin Central Hospital Elazığ23000 Turkey Fırat University Faculty of Engineering Software Engineering Elazığ23119 Turkey

Urine sediment examination (USE) is one of the main tests used in the evaluation of diseases such as kidney, urinary, metabolic, and diabetes and to determine the density and number of various cells in the urine. USE's manual microscopy is a labor-intensive and time-consuming, imprecise, subjective process. Recently, automatic analysis of urine sediment has become inevitable in the medical field. In this study, we propose a dataset that can be used by artificial intelligence techniques to automatically identify particles in urine sediment images. The data set consists of 8509 particle images obtained by examining the particles in the urine sediment obtained from 409 patients from the Biochemistry Clinics of Elazig Fethi Sekin Central Hospital. Particle images are collected in 8 classes in total and these are Erythrocyte, Leukocyte, Epithelial, Bacteria, Yeast, Cylinders, Crystals, and others (sperm, etc.). © 2023, CC BY-NC-ND.

关键词： Sediments

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Using Machine Learning to Enhance Personalization in E-Learning

Using Machine Learning to Enhance Personalization in E-Learn...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Sneha S. Palimkar Kannan Kunnathully Srinivasarao Thota Meeta Joshi Afsha Imran Akkalkot Nidal Al Said Computer Engineering and Technology COEP Technological University Pune Maharashtra Veltech School of Law Software Engineering Capital One Virginia USA Faculty of Management Studies Marwadi University Computer Science Engineering College of Engineering Pune Technological University Pune Maharashtra College of Mass Communication Ajman University Ajman UAE

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Numerous diverse learning materials can be found on e-learning sites. Students in today's e-learning platforms invest a lot of time and energy in locating pertinent learning materials. The student's actual requirements must be taken into account based on a variety of characteristics, including choices, expertise, and learning style. Education must be pertinent to the necessary concept's environment. This study aims to develop an efficient approach for detecting e-learning style and then customizing the e-learning contents to match that style, using machine learning (ML) algorithms to enhance personalization in e-learning. The blended ensemble method combined with the XGBoost meta-learning approach produced the most prominent results for enhancing e-learning style, with an accuracy of 97.6%. Further, the textual material of the e-files is altered using various natural language processing (NLP) approaches. The spaCy NLP-oriented labeled entity identification (LEI) algorithm achieves a 94.2% F1 value and a 0.92 precise match ratio while color-coded textual production of 10 e-files with 790 different phrases. These alterations are intended to suit students' tastes, resulting in an additional personalized and interactive teaching encounter.

关键词： Metalearning Electronic learning Machine learning algorithms Accuracy Databases Education Production Natural language processing Classification algorithms Ensemble learning

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Characterization and Control of a Superconducting Homopolar Motor with Cryogenic GaN Inverter for Electric Aircrafts

Characterization and Control of a Superconducting Homopolar ...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Gliese, Felix Pearce, Matthew G. S. Wadsworth, Aaron Siamaki, Mohammad Storey, James Lumsden, Grant Thrimawithana, Duleepa J. Badcock, Rod Roth-Stielow, Joerg Neuburger, Martin Esslingen University Faculty of Mobility and Technology Germany The University of Auckland Dept. of Electrical Computer and Software Engineering New Zealand OpenStar Technologies New Zealand Victoria University of Wellington Robinson Research Institute New Zealand University of Stuttgart Institute for Power Electronics and Electrical Drives Germany

ISBN: (纸本)9798350376067

In the quest for a zero-emissions future, the aviation sector is facing the challenge of reducing its carbon footprint. This paper presents a characterization of a superconducting homopolar motor designed to meet the stringent power and efficiency requirements of all-electric aircraft. The control system, which features field-oriented control and a multi-layer architecture, ensures precise operation of the motor under various load conditions. The motor uses a stationary cryogenic superconducting DC coil to generate the rotor's magnetic field. Utilizing superconducting materials helps produce the high power densities critical for aviation. The characterization of the key parameters of the motor, such as inductances, stator resistance and flux, are presented. A description of the cryogenic GaN power electronic inverter power stage immersed in liquid nitrogen is provided. This design enables future integration next to the motor, and improved power density and efficiency. This paper outlines the design, implementation, and initial testing of the superconducting homopolar motor and its power electronics, highlighting its potential for electric propulsion in aviation by providing a viable solution for long-range flights. © 2024 IEEE.

关键词： Gallium nitride

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Unveiling Stress: A Comparative Analysis of Multimodal Sensor Fusion Techniques for Predictive Modeling

Unveiling Stress: A Comparative Analysis of Multimodal Senso...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Noha A. Saad Eldien Raghda Essam Ali Mustafa Ezzeldin Moamen Zaher Faculty of Computer Science October University for Modern Sciences and Arts (MSA) Giza Egypt Software Engineering Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

Stress in professional environments is a significant concern. Medical professionals are particularly vulnerable to stress, especially during emergencies. Nurses hold a vital position in delivering care within hospital settings. It's crucial to anticipate stress levels among nurses to help them perform their duties effectively and avoid the long-term impacts of stress. This study seeks to explore how body sensors and machine learning techniques can be employed to monitor physiological signs and identify stress levels among nurses. It utilizes a benchmark dataset collected from 15 different nurses, including signals such as heart rate (HR), Electrodermal Activity (EDA), and Skin Temperature alongside location data extracted by an accelerometer. This study explores several fusion strategies, such as data, model, and prediction fusion levels to improve the accuracy and reliability of stress prediction models. Through a comparative analysis, this paper highlights the strengths and limitations of diverse fusion techniques. shedding light on their efficacy in capturing the nuanced features of stress. The findings offer valuable insights into the optimization of multimodal sensor fusion for enhanced stress prediction, creating pathways for reliable frameworks in the healthcare domain. This research conducted a comparative study between 3 different levels: data-fusion, model-fusion, and prediction-fusion. Prediction-level fusion outperformed both model-level fusion by 1.97% and data-level fusion by 1.26%.

关键词： Temperature sensors Heart rate Temperature measurement Temperature distribution Multimodal sensors Predictive models Data models

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