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SSRN 2023年

作者： Ma, Jun Yu, Yin Xu, Zhaoyang Xu, Lisheng Li, Yikuan Liu, Tianqing Meng, Qingshi Su, Xiao Kuan, Hsu-Chiang Dai, Jiabin College of Medicine and Bioinformatics Engineering Northeastern University Shenyang110819 China College of Aerospace Engineering Shenyang Aerospace University Shenyang110136 China Neusoft Research of Intelligent Healthcare Technology Co. Ltd Shenyang110169 China Key Laboratory of Medical Image Computing Ministry of Education Shenyang110169 China NICM Health Research Institute Western Sydney University Westmead2145 Australia Shenyang Aircraft Design Institute Shenyang110035 China School of Engineering Future Industries Institute University of South Australia Mawson LakesSA5095 Australia Department of Chemical and Materials Engineering Southern Taiwan University of Science and Technology Tainan City710301 Taiwan

Flexible wearable electronic devices offer promising potential for monitoring physiological signals. However, creating a single sensor that integrates high tensile strength, sensitivity, self-healing capabilities, and a wide working range presents a significant and multifaceted challenge. This study reports a novel nanocomposite consisting of polyurethane-urea elastomer (PUU) and graphene nanoplatelets (E-GNPs) that are mechanochemically modified with diethyltoluene diamine ethacure 100. A low electrical percolation threshold was observed at 4.17 vol% E-GNPs. The nanocomposite-based sensor has high stretchability (361.76%), strength (19.73 MPa), and sensitivity (gauge factor up to 17.57). It can withstand 20,000 cycles at 50% strain. The sensor exhibits negative temperature dependence at 20–100 °C, with a resolution of 0.01/°C at 36–40 °C. Treatments with solvents and heat enable a healing efficiency for sensitivity of up to 70.46%. The healable sensor enables real-time monitoring of temperature and strain signals, making it ideal for wearable devices in human health and sports monitoring. © 2023, The Authors. All rights reserved.

关键词： Graphene

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Quantitative comparison of the total focusing method, reverse time migration, and full waveform inversion for ultrasonic imaging

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Ultrasonics 2025年 155卷

作者： Tim Bürchner Simon Schmid Lukas Bergbreiter Ernst Rank Stefan Kollmannsberger Christian U. Grosse Technical University of Munich TUM School of Engineering and Design Chair of Computing in Civil and Building Engineering Arcisstrasse 21 Munich 80333 Germany Technical University of Munich TUM School of Engineering and Design Department of Materials Engineering Chair of Non-destructive Testing Franz-Langinger-Str. 10 Munich 81245 Germany Technical University of Munich Institute for Advanced Study Lichtenbergstrasse 2a Garching 85748 Germany Bauhaus-Universität Weimar Chair of Data Science in Civil Engineering Coudraystraße 13 Weimar 99423 Germany

Phased array ultrasound is a widely used technique in non-destructive testing. Using piezoelectric elements as both sources and receivers provides a significant gain in information and enables more accurate defect detection. The process is called full matrix capture when all source–receiver combinations are used. The total focusing method (TFM) exploits such datasets and relies on a delay and sum algorithm to sum up the signals on a pixel grid. However, TFM only uses the first arriving p-waves, making it challenging to size complex-shaped defects. By contrast, more advanced methods such as reverse time migration (RTM) and full waveform inversion (FWI) use full waveforms to reconstruct defects. Both methods compare measured signals with ultrasound simulations. While RTM identifies defects by convolving forward and backward wave fields once, FWI iteratively updates material models to reconstruct the actual distributions of material properties. This study compares TFM, RTM, and FWI for six specimens featuring circular defects or Y-shaped notches. The reconstructed results are first evaluated qualitatively by visual assessment, then different defect segmentation approaches are studied, and finally, they are evaluated quantitatively using metrics such as AUPRC, AUROC, and F1-score. The results show that a two-step FWI framework performs best in most of the six samples studied, both qualitatively and quantitatively.

关键词： Ultrasound Reconstruction Phased array Full matrix capture Wave simulation Total focusing method Reverse time migration Full waveform inversion

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UNDERSTANDING THE INTERACTION BETWEEN VIRTUAL design, CONSTRUCTION AND LEAN CONSTRUCTION 29

UNDERSTANDING THE INTERACTION BETWEEN VIRTUAL DESIGN, CONSTR...

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29th Annual Conference of the International Group for Lean Construction, IGLC 2021

作者： Rodriguez, Maria Guadalupe Mandujano Cardenas, Luis Fernando Alarcon Dave, Bhargav A. Mourgues, Claudio Koskela, Lauri MARO Consulting Huejutla de Reyes Hidalgo Mexico Dept. of Construction Engineering and Management Pontifica Universidad Católica de Chile Escuela de Ingeniería Casilla 360 Correo 22 Santiago de Chile Chile Dept. of Computing Aalto Univ. Espoo02150 Finland School of Art Design and Architecture Univ. of Huddersfield Huddersfield HD1 3DH United Kingdom

ISBN: (纸本)9786124802546

There have been important advances regarding the synergies between Building Information Modeling (BIM) - as part of Virtual design and Construction (VDC) - and Lean Construction. However, the literature does not fully explore the nature of these synergies nor the conceptual reasons behind them. This better understanding of these synergies would allow the Architecture, engineering and Construction (AEC) industry to achieve better Lean and VDC implementations and would provide a stepping-stone for the academia to continue building on these synergies. This article presents a thorough literature review based on leading international journals, conference proceedings and books, to explore the synergies between Lean Construction and VDC, including BIM (product), process and organization modeling. As part of this review, the article tests mechanisms about interaction mechanisms, previously mentioned in the literature. The findings indicate that using the entire VDC framework, the positive interactions between Lean and VDC increased significantly with respect to the same analysis restricted to the interaction between Lean and BIM. Identifying these new interactions and interaction mechanisms can help the AEC industry take a more holistic approach and generate improvements in every project phase. © Prof. Luis F. Alarcón and Assoc. Prof. Vicente González, IGL2021 All rights reserved.

关键词： Architectural design

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SCNet: Few-shot image classification via self-correlational and cross spatial-correlation attention

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engineering Science and Technology, an International Journal 2025年 67卷

作者： He, Congqing Xu, Ding Gong, Ke Guo, Fusen Wei, Dapeng School of Computer Sciences Universiti Sains Malaysia Penang 11800 Malaysia Computer Science Department Harbin Institute of Technology Harbin 150000 China College of Mechanical Engineering North University of China Taiyuan 030000 China School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne 3122 Australia School of Design & Arts Beijing Institute of Technology Beijing 100081 China

Recently, few-shot learning has gained significant attention across various industries due to its ability to rapidly adapt and learn new tasks with few labeled training data. However, existing methods often struggle with capturing feature diversity within classes and aligning features between support and query images, leading to sub-optimal performance. To tackle these challenges, we propose SCNet, a novel few-shot image classification framework that leverages self-correlational and cross spatial-correlation attention mechanisms to enhance feature extraction and alignment. Specifically, we introduce a Self-Correlational Attention module that focuses on critical local features by locating target regions within basic image features, enhancing the extraction of discriminative features from each image independently. Additionally, we design a Cross Spatial-Correlation Attention module to capture shared features between support and query images, generating an informative co-attention map that improves feature alignment across different images. Furthermore, we introduce a novel loss function that combines label-based classification loss and metric-based loss, which mitigates overfitting during meta-testing and enhances alignment of query embeddings with category prototypes. Experiments on three few-shot benchmark datasets show that the proposed SCNet model significantly outperforms state-of-the-art few-shot image classification models. © 2025

关键词： Cross spatial-correlation attention Few-shot image classification Label-based classification loss Metric-based loss Self-correlational attention

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Urban trip generation forecasting based on gradient boosting algorithm 1

Urban trip generation forecasting based on gradient boosting...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Li, Zhishuai Xiong, Gang Zhang, Yu Zheng, Meng Dong, Xisong Lv, Yisheng Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Municipal Institute of City Planning and Design Traffic Planning Department Beijing China

ISBN: (纸本)9781665433372

The four-step transportation model plays an important role in urban planning. The quality of the first phase, i.e. trip generation, determines the performance of the global course. The majority of trip generation forecasting models highly rely on mathematical derivation and have many predictor variables during the prediction, which leads to low accuracy of results and requires laboriously hand-crafted design of input vectors. This paper is the first to introduce the gradient boosting decision tree (GBDT) algorithm for trip generation prediction, and harmonizes such a powerful machine learning method with traditional urban planning requirements to achieve better prediction performance. Unlike the commonly used linear regression method, GBDT can automatically perform feature selection and model the non-linear relationships between input and output variables. Experimental results on real-world residential travel census in Beijing prove that the GBDT model significantly outperforms the baseline and can forecast the trip generation more accurately. © 2021 IEEE.

关键词： Decision trees

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Intelligent HMI and Control for Steering a Powered Wheelchair Using a Raspberry Pi Microcomputer

Intelligent HMI and Control for Steering a Powered Wheelchai...

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International IEEE Conference on Intelligent Systems, IS

作者： Malik Haddad David Sanders Favour Ikwan Mohamad Thabet Martin Langner Alexander Gegov School of Mechanical and Design Engineering University of Portsmouth Portsmouth UK Chailey Heritage Foundation / Engineering Chailey Heritage Foundation Chailey UK School of Computing University of Portsmouth Portsmouth

ISBN: (数字)9781728154565

ISBN: (纸本)9781728154572

This paper presents a new technique for controlling powered wheelchairs. A Raspberry Pi microcomputer is used to assist in controlling direction. A Raspberry Pi is inserted between user input switches and powered wheelchair motors to create a more intelligent Human Machine Interface (HMI). An electronic circuit is created that consists of an ultrasonic sensor array and a set of control relays. The sensors provided information about obstacles surrounding the wheelchair. Python programming language was used to create a code that digitized the output from the user switches and assessed information provided by the ultrasonic sensor array. The code was loaded onto a Raspberry Pi and the Raspberry Pi controlled voltages supplied to the motors. Tests were conducted and results showed that the new system can successfully assist a wheelchair user in avoiding obstacles. The system can be used as an intelligent interface between any input device or sensor system and wheelchair motors.

关键词： Wheelchairs Acoustics Relays Sensor arrays Python Microcomputers

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Wireless Acoustic Sensor Networks and Edge computing for Rapid Acoustic Monitoring

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 64-74页

作者： Zhengguo Sheng Saskia Pfersich Alice Eldridge Jianshan Zhou Daxin Tian Victor C.M.Leung the Department of Engineering and Design University of Sussex Infineon Technologies AG the Department of Music University of Sussex Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety ControlSchool of Transportation Science and EngineeringBeihang University the Department of Electrical and Computer Engineering The University of British Columbia

Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity *** scale of the data involved requires automated methods,however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive,hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ *** and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination *** tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions.

关键词： Acoustic sensor networks edge computing energy efficiency

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Steering Direction for a Powered-Wheelchair Using the Analytical Hierarchy Process

Steering Direction for a Powered-Wheelchair Using the Analyt...

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International IEEE Conference on Intelligent Systems, IS

作者： Malik Haddad David Sanders Martin Langner Favour Ikwan Giles Tewkesbury Alexander Gegov School of Mechanical and Design Engineering University of Portsmouth Portsmouth UK Chailey Heritage Foundation / Engineering Chailey Heritage Foundation Chailey UK School of Computing University of Portsmouth Portsmouth UK

ISBN: (数字)9781728154565

ISBN: (纸本)9781728154572

For the first time, the Analytical Hierarchy Process (AHP) is employed to recommend a steering course for a powered-wheelchair. A safe route for a wheelchair is proposed by the decision-making system that tends to evade obstacles. Two ultrasonic transceivers are mounted onto a wheelchair. The resulting route is a blend between a provided direction from a user's joystick and a proposed direction from the decision making system that steers a powered-wheelchair to safely avoid obstacles in the way of the wheelchair. The procedure assists a disabled user to steer their wheelchair by choosing a direction that is a compromise between a direction suggested by the sensors and a direction requested by the driver. Sensitivity analysis explores directions amid uncertainty and risk. An appropriate direction is chosen but a human driver can over-ride the decision if necessary.

关键词： Wheelchairs Sensitivity analysis Acoustics Decision making Sensor systems Wheels

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Initial Results from Using an Intelligent System to Analyse Powered Wheelchair Users’ Data

Initial Results from Using an Intelligent System to Analyse ...

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International IEEE Conference on Intelligent Systems, IS

作者： Malik Haddad David Sanders Martin Langner Peter Omoarebun Mohamad Thabet Alexandar Gegov School of Mechanical and Design Engineering University of Portsmouth Portsmouth UK Chailey Heritage Foundation / Engineering Chailey Heritage Foundation Chailey UK School of Computing University of Portsmouth Portsmouth UK

ISBN: (数字)9781728154565

ISBN: (纸本)9781728154572

Research in this paper presents a technique to collect powered wheelchair users' data using an intelligent system. Python programming language is used to create a program that will collect data for future analysis. The collected data considers driving session details, medication administered that can affect driving ability, and the type of input devices used to control a powered wheelchair. Data is collected on a Raspberry Pi microcomputer and is sent after each session via email. Data is placed in the body of the emails and in an attached file. Data will be used for future analysis and will be considered as a training data set to train an intelligent system to predict future route patterns for different wheelchair users. In addition, data will be used to analyze the ability of a user to operate their wheelchair, and monitor users' progress from one session to another, compare the progress of different users with the same type of disability and identify the most suitable input device for each user and route.

关键词： Wheelchairs Electronic mail Input devices Intelligent systems Python design engineering Wheels

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Lessons Learnt from designing a Smart Clothing Telehealth System for Hospital Use 20

Lessons Learnt from Designing a Smart Clothing Telehealth Sy...

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Proceedings of the 32nd Australian Conference on Human-Computer Interaction

作者： Deepti Aggarwal Thuong Hoang Bernd Ploderer Frank Vetere Rohit Ashok Khot Mark Bradford HAFP Research Lab RMIT University Australia School of Information Technology Deakin University Australia School of Electrical Engineering and Computer Science Queensland University of Technology Australia Interaction Design Lab School of Computing and Information Systems The University of Melbourne Australia Dept. of Anaesthesia and Pain Management Royal Children's Hospital Australia

ISBN: (纸本)9781450389754

In this paper, we describe the design journey of a smart clothing system, SoPhy from the research laboratory to finally being evaluated in the hospital setting. SoPhy is a smart socks-based system, designed to make physiotherapy video consultations effective for assessing lower limb issues. SoPhy is the result of a 3-year journey of development, study and refinement of a research prototype done in collaboration with a physiotherapist. Drawing on this journey, we present seven lessons that emphasize on the importance of fostering strong collaborations with clinicians to move beyond laboratory studies and reach the target health setting. We provide contextual narratives on how we designed a comfortable smart clothing for patients; how we created an intuitive mapping of sensor data for clinicians; how we integrated hospital practice in the system design, and how we managed the ethics clearance for the field evaluation. We hope that these lessons are useful for HCI and health researchers who aim to innovate the field of health but feel restricted with the little knowledge of design and electronics.

关键词： Telehealth Physiotherapy Video Consultations Smart Clothing Digital health

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