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Effects of ultrasonic vibration parameters on the cutting force, chip morphology, and bone cracking in bovine cortical bone milling

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International Journal of Abrasive technology 2024年第4期12卷 293-312页

作者： Hu, Yahui Zhong, Jie He, Lingrui Lin, Yize Zhang, Zhaoxiong Zhang, Chunqiu Fu, Weihua Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China Department of General Surgery Tianjin Medical University General Hospital Tianjin300052 China

Ultrasonic vibration-assisted technology is employed in orthopaedic surgery to minimise invasive effects in bone milling. This study explored the motion characteristics of ultrasonic vibration-assisted milling (UVAM), its intermittent cutting phenomenon, and tool-bone contact and separation times, via the developed experimental platform. The UVAM and conventional milling (CM) tests were carried out on bovine cortical bones with measurement of respective cutting forces and scanning electron microscope (SEM) observation of crack initiation and propagation in the milled bones. The UVAM application improved the chip-breaking ability of the milling tool and reduced the bone-milling force by 15.90%–25.06% compared to CM. Properly adjusting ultrasonic frequency and amplitude made it possible to reduce the bone cracking effect and enhance the milled bone surface quality. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Mammals

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Wearable Biosensors: Transformative Innovations and Future Pathways for Continuous Health Monitoring 2

Wearable Biosensors: Transformative Innovations and Future P...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Thute, Trupti Gudadhe, Amit Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Department of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Department of Computer Science and Design Maharashtra Wardha India

ISBN: (纸本)9798331505745

Wearable biosensors are revolutionizing healthcare by enabling continuous monitoring of health status, transforming it from an event-based process to an ongoing one. When integrated with AI, nanotechnology, and IoMT (Internet of Medical Things), these devices enhance precision care, telediagnosis, and chronic disease management. Advancements in electrochemical, optical, and biochemical sensors, along with innovations in energy harvesting and miniaturized flexible materials, improve both the performance and comfort of these devices. Wearable biosensors are now crucial for everyday fitness, disease prediction, and the seamless exchange of data between m-health apps and EHRs. Despite these advancements, challenges remain, such as data security, system integration, and user incentives. Addressing these concerns through enhanced security features, standardized protocols, and user-focused designs is critical. The future development of wearable biosensors is aimed at leveraging cost-effective energy sources, utilizing AI for data analysis, and improving user convenience, making significant strides in preventive and personalized healthcare. © 2025 IEEE.

关键词： mHealth

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Bayesian Updating of Soil Parameters in Ground Settlement of Reclaimed Land 34th

Bayesian Updating of Soil Parameters in Ground Settlement of...

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34th International Ocean and Polar engineering Conference, ISOPE 2024

作者： Chian, Siau Chen Leung, Chun Fai Shen, Zhichao Department of Civil and Environmental Engineering College of Design and Engineering National University of Singapore Singapore Singapore School of Marine Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781880653784

In geotechnical coastal engineering, estimates of ground settlement of reclaimed land can be challenging especially those made from heterogeneous compressible fill materials. Boreholes conducted to retrieve soil samples can only reflect the ground profile at that particular location that may not be representative a few metres away. To complement numerical estimates, observational methods are often carried out over a considerable duration of months to years to monitor the settlement over time. In this paper, advancement in numerical simulation of ground settlement with soil parameters inference and updating is introduced to improve the effectiveness of traditional observational methods. This involves the use of Bayesian updating to conduct back-analysis to refine soil parameters so as to yield closer representation of the actual ground conditions. With increasing survey measurements of actual ground settlement, the prediction of ground settlement would get more accurate over time since the soil parameters would gradually converge to the actual field condition. Polynomial chaos expansion based surrogate models are applied to overcome the low efficiency of conventional numerical software packages during the backanalysis iterations. © 2024 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Numerical methods

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Front-light Structure for a Lensless Fluorescence Imaging Device with a Hybrid Emission Filter

Front-light Structure for a Lensless Fluorescence Imaging De...

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2023 IEEE Biomedical Circuits and systems Conference, BioCAS 2023

作者： Sasagawa, Kiyotaka Ito, Yuki Schaeffer, Daniel Sunaga, Yoshinori Takehara, Hironari Haruta, Makito Tashiro, Hiroyuki Ohta, Jun Deggendorf Institute of Technology Deggendorf Germany Nara Institute of Science and Technology Graduate School of Science and Technology Department of Materials Science Nara Japan Chitose Institute of Science and Technology Faculty of Science and Engineering Department of Opto-Electronic System Engineering Japan Kyushu University Faculty of Medical Sciences Department of Health Science Japan

ISBN: (纸本)9798350300260

Lensless optical imaging is extensively used for deep brain imaging. In this study, we propose and demonstrate a front-light structure to improve the fluorescence observation performance of a lensless fluorescence imaging device. A thin front-light structure is inserted between the object to be observed and the emission filter. This enables the irradiation of excitation light from the same side as the lensless imaging device. We fabricated a front-light structure by patterning diffraction gratings on a thin glass and combined it with a high-performance emission filter to obtain a fluorescence imaging device, which facilitates fluorescence observation as well. © 2023 IEEE.

关键词： fluorescence imaging front-light hybrid emission filter lensless imaging

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Dysarthric speech recognition: an investigation on using depthwise separable convolutions and residual connections

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Neural Computing and Applications 2025年第12期37卷 7991-8005页

作者： Shahamiri, Seyed Reza Mandal, Krishnendu Sarkar, Sudeshna DeepNet Discovery Network Faculty of Engineering and Design University of Auckland Private Bag 92019 Building 405 Level 6 Room 669 3 Garfton Road Auckland1142 New Zealand Computer Science and Engineering Department Indian Institute of Technology Kharagpur India

Dysarthria is a neurological condition resulting from impairments affecting muscle control involved in speech articulation, leading to reduced intelligibility or unintelligible speech, which affects communication abilities. Although Automatic Speech Recognition (ASR) technologies hold the potential to improve the lives of people with dysarthria significantly, ASR systems designed for normal speech have shown limited effectiveness when presented with impaired speech. Consequently, researchers have focused on developing ASR systems specifically tailored for dysarthria. However, progress in this area has been gradual due to the scarcity of dysarthric speech for training and the increased variability of speech among dysarthric individuals, necessitating a larger dataset of dysarthric utterances. One potential solution to enhance the robustness of dysarthric ASR is to deepen the architecture of the acoustic model, which maps the speech signal to words or phonetic units. However, deeper architectures require more training data and pose challenges in dealing with issues such as the vanishing gradient problem and representational bottlenecks in deep learning models. In this study, we expanded on our previous findings and investigated the applications of Depthwise Separable Convolution neurons and the inclusion of Residual Connections to propose a deep dysarthric acoustic model, tackling both vanishing gradients and representational bottleneck issues in dysarthric ASR. Multiple speaker-adaptive dysarthric ASRs were trained and evaluated for 15 UA-Speech dysarthric subjects, then benchmarked against the state-of-the-art and our previous dysarthric ASRs. Our proposed architectures have delivered up to 22.58% word recognition rate (WRR) improvements over the reference models. We observed an average of 10.81% better WRRs over the base traditional dysarthric ASR for all speakers. Likewise, the proposed acoustic model outperformed the state-of-the-art Transformer-based dysarthric

关键词： Convolution

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Multimodal element(including lithium)mapping in a Mg-9Li-4Al-1Zn alloy

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Journal of Magnesium and Alloys 2024年第11期12卷 4468-4480页

作者： Yu Zhang Yang Liu Zhuoran Zeng Nick Birbilis Philip N.H.Nakashima Laure Bourgeois Department of Materials Science and Engineering Monash UniversityVIC 3800Australia Monash Centre for Electron Microscopy Monash UniversityVIC 3800Australia College of Materials Science and Engineering Hunan UniversityChangsha 410082China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan UniversityChangsha 410082China Suzhou Research Institute of Hunan University Suzhou 215131China Faculty of Science Engineering and Built EnvironmentDeakin UniversityVIC 3125Australia

Determining the distribution of alloying elements,particularly lithium,is crucial for a holistic understanding of magnesium-lithium-based *** this work,a bespoke ratio spectrum-imaging method based on electron energy-loss spectroscopy,in combination with time-of-flight secondary ion mass spectrometry,energy-dispersive X-ray spectroscopy and Z-contrast imaging,was applied to an as-rolled LAZ941 alloy(Mg-9Li-4Al-1Zn in wt.%).This was done to characterize the distribution of alloying elements,including the distribution of solute in the magnesium *** applications of different mapping techniques revealed that precipitates with two different morphologies are rich in Li,Al and Zn,compared to their surrounding ***,it was confirmed that theβ-phase of the alloy contains higher Li and lower Mg concentrations when compared to theα-*** study demonstrated the effectiveness and accuracy of the ratio spectrum-imaging method for mapping the elemental distribution(including lithium)in a range of Li-containing materials.

关键词： Magnesium alloys Lithium distribution mapping Solute content mapping Phase determination Electron energy-loss spectroscopy(EELS) Time-of-flight secondary ion mass spectrometry(ToF-SIMS) Scanning transmission electron microscopy(STEM)

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Overcoming Spatial Constraints in VR:A Survey of Redirected Walking Techniques

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Journal of Computer science & technology 2024年第4期39卷 841-870页

作者： Jia-Hong Liu Yang-Fu Ren Qi Wen Gan Kui Huang Fiona Xiao Yu Chen Er-Xia Luo Khang Yeu Tang Yue-Yao Fu Cheng-Wei Fan Sen-Zhe Xu Song-Hai Zhang Academy of Arts and Design Tsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China College of Computer Technology and Application Qinghai UniversityXining 810016China Zhili College Tsinghua UniversityBeijing 100084China School of Computer Beijing Information Science and Technology UniversityBeijing 100192China Department of Physics Tsinghua UniversityBeijing 100084China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

As the virtual reality(VR)technology strives to provide immersive and natural user experiences,the challenge of aligning vast virtual environments with limited physical spaces remains *** survey comprehensively explores the advancements in redirected walking(RDW)techniques aimed at overcoming spatial constraints in *** addresses this by subtly manipulating users’physical movements to allow for seamless navigation within constrained *** survey delves into gain perception mechanisms,detailing how slight discrepancies between virtual and real-world movements can be utilized without user awareness,thus extending the effective navigable *** RDW control algorithms for gain-based RDW are analyzed,highlighting their implementation and effectiveness in maintaining immersion and minimizing perceptual ***,novel methods extending beyond traditional gain-based techniques are discussed,showcasing innovative approaches that further refine VR *** practical implications of RDW in enhancing safety and reducing physical collisions in VR environments are underscored,alongside its potential to improve user experience by aligning virtual exploration more closely with natural human behavior *** a thorough review of existing literature and recent advancements,this survey provides a systematic understanding for researchers,developers,and industry *** underscores the importance of RDW in the future of VR,emphasizing RDW's role in making VR more accessible and practical across various applications,from education and training to therapy and *** paper concludes with a forward-looking perspective on the continued evolution and potential of RDW in revolutionizing virtual reality experiences.

关键词： virtual reality(VR) redirected walking(RDW) redirection gain perception

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Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai

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International Journal of Transportation science and technology 2024年第1期13卷 106-121页

作者： Yu Shen Chenlong Xu Shengchuan Jiang Zhikang Zhai Yuxiong Ji Yuchuan Du Key Laboratory of Road and Traffic Engineering of the MOE Tongji Unviersity4800 Cao’an Hwy.Shanghai 201804China Department of Transportation System Engineering Business SchoolUniversity of Shanghai for Science and Technology516 Jungong Rd.Shanghai 200093China

This work attempts to understand how a customized bus(CB)operator decides to open or close a CB *** look into the changes in the operation status of CB lines(*** and closure)from one of the largest CB operators in Shanghai,China,with a 22-month con secutive observation ranging from January 2019 to October *** all CB services were totally suspended at the beginning of 2020 due to the COVID-19 travel restriction and then gradually recovered in March 2020,we utilize this study period as a naturalistic observa tion experiment to investigate the changes in the operation status of each CB line before and after the travel *** the operation status at each month as the binary alternatives,the mixed logit models and the tree-based models with explainable machine learning techniques are respectively adopted to explore the factors that influence the decision-making *** findings from both types of models are in general *** results show that the characteristics of each CB line including the ridership,the length of the line,the closeness to charging stations,and the overlap of CB lines significantly impact the *** addition,the land-use types around the CB stops and the market competition from alternative travel modes also play a key role in making the decisions.

关键词： Customized bus Decision-making Naturalistic observations Discrete choice models Explainable machine learning

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Automated Breast Cancer Diagnosis Using Deep Learning CNN Models 2

Automated Breast Cancer Diagnosis Using Deep Learning CNN Mo...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Saravanan, G. Jeevanantham Prabin, S.M. Parthasarathy, R. VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Design Chennai India

ISBN: (纸本)9798331534950

The study introduces a deep learning model, CCNN, developed to identify and classify eight types of breast cancer: normal adenosis, normal fibroadenoma, normal phyllodes tumor, normal tubular adenoma, malignant ductal carcinoma, malignant lobular carcinoma, malignant mucinous carcinoma, and malignant papillary carcinoma. This model was integrated with five pre-existing models - Xception, InceptionV3, VGG19, MobileNetV2, and ResNet101 - all trained for image classification using the ImageNet database, to categorize breast cancer MRI images. The dataset, sourced from Kaggle's repository, was further augmented using the Generative Adversarial Network (GAN) technique. For 30 experiments carried out to evaluate the introduced model and the transfer learning of five and proposed CCNN models, the F1-score values accuracy yielded were respectively: 98.04%, 96.13%, 98.54%, 97.11%, 94.20% and 98.98%. This study emphasizes the importance of improving the accuracy of early breast cancer detection to enhance the survival rates of patients. © 2024 IEEE.

关键词： Generative adversarial networks

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Real-Time Road Damage Detection and Infrastructure Evaluation Leveraging Unmanned Aerial Vehicles and Tiny Machine Learning

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IEEE Internet of Things Journal 2024年第12期11卷 21347-21358页

作者： Khan, Muhammad Waseem Obaidat, Mohammad S. Mahmood, Khalid Batool, Dania Badar, Hafiz Muhammad Sanaullah Aamir, Muhammad Gao, Wu Comsats University Islamabad Sahiwal Campus Department Of Computer Science Sahiwal57000 Pakistan Superior University Department Of Computer Science Lahore54000 Pakistan The University Of Jordan King Abdullah Ii School Of Information Technology Amman11942 Jordan University Of Science And Technology Beijing School Of Communication And Computing Engineering Beijing100083 China Amity University Amity School Of Engineering And Technology Uttar Pradesh Noida201301 India National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan University Of Palermo Department Of Engineering Palermo90133 Italy Emerson University Department Of Emerging Computing Technologies Multan60000 Pakistan Northwestern Polytechnical University Center For Bio-Inspired System-on-Chip School Of Computer Science Xi'an710060 China

Road damage detection (RDD) through computer vision and deep learning techniques can ensure the safety of vehicles and humans on the roads. Integrating unmanned aerial vehicles (UAVs) in RDD and infrastructure evaluation (IE) has also emerged as a key enabler, contributing significantly to data acquisition and real-time monitoring of road damages, such as potholes, cracks, and surface anomalies, facilitating proactive maintenance and improved road conditions. These UAVs are low-powered and resource-constrained devices that work autonomously to perform pattern detection and decision making leveraging tiny machine learning (Tiny ML) algorithms. These Tiny ML algorithms are designed to run on edge devices, IoT devices, UAVs, etc. In this study, the RDD2022 data set collected using UAVs and dashboard cameras of vehicles was utilized to train pure and mixed models that exhibit class instance imbalance in certain classes which is addressed by implementing data augmentation as a regularization technique. State-of-the-art two-stage detectors: Faster R-CNN ResNet101 and one-stage detectors: SSD MobileNet V1 FPN, YOLOv5, and Efficientdet D1 are employed. The results indicate that the two-stage detector achieved an impressive mean average precision (mAP) of 88.49% overall and 96.62% for focused classes. Notably, the state-of-the-art Efficientdet D1 approach achieved a competitive mAP of 86.47% overall and 95.12% for focused classes, with significantly lower computational cost. These findings highlight the potential of advanced object detection techniques, particularly Efficientdet D1, to enhance the accuracy and efficiency of RDD systems, thereby improving passenger safety and overall performance. © 2014 IEEE.

关键词： Object detection

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