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5th International Conference on Building Energy and Environment, COBEE 2022

作者： Bale, Rahul Li, Chungang Fukudome, Hajime Yumino, Saori Iida, Akiyoshi Tsubokura, Makoto RIKEN Center for Computaitonal Science Kobe Japan Graduate School of System Informatics Kobe Univeristy Kobe Japan Department of Mechanical Engineering Toyohashi Institue of Technology Toyohashi Japan Kajima Corporation Tokyo Japan Numerical Flow Design Tokyo Japan

ISBN: (纸本)9789811998218

An activity wherein the usage of masks and social distancing, to prevent the spread of COVID-19 may not be possible, is not possible is during social gatherings over a meal or a drink. Droplets generated during a casual conversation during a meal can lead to the transmission of COVID, due to which restaurants and bars can turn out to be vectors for its transmission. Therefore, it is necessary to investigate the dispersion of respiratory droplets in such an environment. To that end, we carry out numerical simulations of droplets dispersion in a typical Japanese restaurant. The role of ventilation and air-conditioning systems on dispersion will be discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Empirical Analysis of Hybrid Machine Learning based Earlier Stage Covid Disease Prediction using Lung CT Images

Empirical Analysis of Hybrid Machine Learning based Earlier ...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2022

作者： Venkatesh, J. Titus, Anita Arthi, B. Jebastine, J. Shaik, Subhani Sakthivel, E. Chennai Institute of Technology Center for System Design Tamil Nadu Chennai India Jeppiaar Engineering College Department of ECE Tamil Nadu Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of CSE Tamil Nadu Chennai India St.Mary's Group of Institutions Department of CSE Andhra Pradesh Guntur India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Corona Virus or Covid-19 Disease, a term which creates a mass affection to all the countries in both Human life as well as economy wise. This disease causes a huge destruction in many person's individual life and most of the people around the world died due to this cause. Several assessments and researches are going on to predict the disease in fine manner as well as identify the disease over earlier stages to save the life of people without any delay. However, the most prominent and acceptable way of predicting the Corona Virus is by using the Lung Computed Tomography (CT) images. The Lung based CT images provides a huge support to identify the Covid virus on earlier stages, in which the people are advised to take such type of scanning while infected with corona virus. An earlier stage identification of Corona Virus is the basic need now-a-days, in which the disease is identified initially, means it can easily be cured. The identification of Covidvirus over lung CT images is of course a complex task because the CT images contains low-intensity pixels and the contrast level variations are different on various images. So it is complex to manipulate such images in practical, due to this a novel Digital Image Processing scheme is required to provide an efficient support to the respective physician to identify the Corona Virus on earlier stages in clear manner. The concept of machine learning is adopted over this paper to provide a proper predictions as well as the logic of dual classification algorithms are combined together to form a new machine learning strategy to attain high accuracy with enhanced prediction probabilities. The logic of Deep Neural Network (DNN) is modulated with respect to the logic of Random Forest (RF) Classification algorithm to make a new methodology called Hybrid Learning based Disease Prediction Scheme (HLDPS). In which this proposed approach associates the benefits of both DNN and RF into this prediction strategy to make an appropriate predi

关键词： Deep neural networks

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Mechanical Properties and Thermal Shock Resistance of SrAl_(2)Si_(2)O_(8) Reinforced BN Ceramic Composites

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无机材料学报 2024年第10期39卷 1182-1188页

作者： WANG Bo CAI Delong ZHU Qishuai LI Daxin YANG Zhihua DUAN Xiaoming LI Yanan WANG Xuan JIA Dechang ZHOU Yu International Joint Laboratory of Advanced Nanomaterials of Heilongjiang Province College of Materials Science and Chemical EngineeringHarbin Engineering UniversityHarbin 150001China Key Laboratory of Advanced Structural-Functional Integration Materials School of Materials Science and EngineeringHarbin Institute of TechnologyHarbin 150001China China Resources Cement Technology R&D Co. LtdGuangzhou 510000China The First Military Representative Office of the Army Equipment Department in Beijing Beijing 100072China The Fourth System Design Department Fourth AcademyChina Aerospace Science and Industry Corporation LimitedBeijing 100048China School of Materials Science and Engineering Harbin Institute of Technology(Shenzhen)Shenzhen 518055China

Hexagonal boron nitride(h-BN)ceramics have become exceptional materials for heat-resistant components in hypersonic vehicles,owing to their superior thermal stability and excellent dielectric ***,their densification during sintering still poses challenges for researchers,and their mechanical properties are rather *** this study,SrAl_(2)Si_(2)O_(8)(SAS),with low melting point and high strength,was introduced into the h-BN ceramics to facilitate the sintering and reinforce the strength and ***,BN-SAS ceramic composites were fabricated via hot press sintering using h-BN,SrCO_(3),Al_(2)O_(3),and SiO_(2) as raw materials,and effects of sintering pressure on their microstructure,mechanical property,and thermal property were *** thermal shock resistance of BN-SAS ceramic composites was *** show that phases of as-preparedBN-SAS ceramic composites are h-BN and h-SrAl_(2)Si_(2)O_(8).With the increase of sintering pressure,the composites’densities increase,and the mechanical properties shew a rising trend followed by a slight *** a sintering pressure of 20 MPa,their bending strength and fracture toughness are(138±4)MPa and(1.84±0.05)MPa·m^(1/2),*** sintered at 10 MPa exhibit a low coefficient of thermal expansion,with an average of 2.96×10^(-6) K^(-1) in the temperature range from 200 to 1200℃.The BN-SAS ceramic composites prepared at 20 MPa display higher thermal conductivity from 12.42 to 28.42 W·m^(-1)·K^(-1) within the temperature range from room temperature to 1000℃.Notably,BN-SAS composites exhibit remarkable thermal shock resistance,with residual bending strength peaking and subsequently declining sharply under a thermal shock temperature difference ranging from 600 to 1400℃.The maximum residual bending strength is recorded at a temperature difference of 800℃,with a residual strength retention rate of 101%.As the thermal shock temperature difference increase,the degree of oxida

关键词： BN matrix composite hot-press sintering mechanical property thermal shock resistance service reliability

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Topological design of cellular structures for maximum shear modulus using homogenization SEMDOT

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Materials Today: Proceedings 2023年 101卷 38-42页

作者： Fu, Yun-Fei Long, Kai Zolfagharian, Ali Bodaghi, Mahdi Rolfe, Bernard Institute for Frontier Materials Deakin University Geelong 3216 Australia School of Engineering Faculty of Science Engineering and Built Environment Deakin University Geelong 3216 Australia State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing 102206 China Department of Engineering School of Science and Technology Nottingham Trent University Nottingham NG11 8NS UK

This paper incorporates the homogenization theory into non-penalized Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT) algorithm to conduct the design of cellular structures with the maximum shear modulus. The parametric study and comparison with existing results obtained by BESO are carried out. The numerical examples in 2D and 3D demonstrate the effectiveness of non-penalized SEMDOT in generating smooth cellular structures with the maximum shear modulus. Compared to BESO, SEMDOT can achieve comparable results and smoother boundaries. Smooth boundaries obtained by SEMDOT can facilitate the manufacturing of obtained cellular structures in 3D or 4D printing. © 2023 Elsevier Ltd. All rights reserved.

关键词： Topology

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Bitcoin Price Prediction Using Deep Learning Models

Bitcoin Price Prediction Using Deep Learning Models

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Rasika Hajare Chetan Puri Pradnyawant M. Gote Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Forecasting the price of bitcoins is significant in contemporary research, given the fact that the digital currency is relatively unpredictable and highly integrated in global securities markets. This paper compares the use of three deep learning models, namely recurrent neural networks (RNN), gated recurrent unit (GRU), and a convolutional neural network-long short-term memory (CNN-LSTM) for Bitcoin price prediction. Historical Bitcoin price data is used in the dataset, and every one of the models is trained and tested based on its performance to predict future prices based on past data. The proposed models include the RNN and GRU, which incorporate sequential dependency mappings to learn complex sequences, and the CNN-LSTM structures combine convolution layers for feature extraction and LSTM layers for sequencing. Different models' competency is discussed using the averaged Mean Squared Error (MSE) and graphs of the model's prediction against actual prices. The experimental results reveal that the proposed CNN-LSTM model has better prediction accuracy than RNN and GRU in time-series forecasting tasks, indicating that more complex architecture hybrid models might provide better forecasting performance. In analyzing and analyzing cryptocurrency price dynamics, this investigation gives information about the opportunities and challenges of these models to enhance future research.

关键词： Deep learning Analytical models Sequential analysis Accuracy Time series analysis Bitcoin Predictive models Convolutional neural networks Forecasting Long short term memory

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An Ontology-based Knowledge Modeling towards Eco-design for Additive Manufacturing 31

An Ontology-based Knowledge Modeling towards Eco-Design for ...

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31st CIRP Conference on Life Cycle engineering, LCE 2024

作者： Wang, Yanan Peng, Tao Kim, Samyeon Xiong, Yi Tang, Yunlong Tang, Renzhong State Laboratory of Fluid Power Components and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Department of Mechanical Engineering Jeonju University Jeonju-si Jeollabuk-do55069 Korea Republic of School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China Mechanical and Aerospace Engineering Department Monash University Melbourne3800 Australia Materials Science and Engineering Department Monash University Melbourne3800 Australia Zhejiang Province Key Laboratory of Advanced Manufacturing Technology School of Mechanical Engineering Zhejiang University Hangzhou310058 China

Owing to the unique technological merits of additive manufacturing (AM), design for AM, particularly eco-design for AM, has been gaining attention in improving the sustainable performance of AM-fabricated (AMed) parts. The main challenge in harvesting potential sustainability benefits is how to capture the impact of design on sustainable performance. Though several existing studies adopted quantitative evaluation on AM sustainability and investigated the relationships between design information and sustainability information of AMed part, the sustainability information is still unavailable in the design phase. This hardly guides eco-design for AM in practice. To support the increasing industrial applications of AM, this paper illustrates related knowledge, that is, material-process-structure-ecoProperty (MPSeP) relationships to connect design and sustainability information in eco-design for AM. An ontology-based method is proposed to model the MPSeP relationships of the AMed part in a structured way. An example of an AMed hydraulic valve body was used to demonstrate the MPSeP modeling process. It is discussed that how the constructed model effectively assists AM designers through providing eco-design recommendation, and enables design software tools to perform sustainability analysis towards eco-design for AM. © 2024 The Author(s).

关键词： 3D printing

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Recent Studies of Human Limbs Rehabilitation Using Mechanomyography Signal: A Survey 4th

Recent Studies of Human Limbs Rehabilitation Using Mechanomy...

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4th International conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

作者： Daud, Muhamad Aliff Imran Ahmad Puzi, Asmarani Sidek, Shahrul Na’im Abu Hassan, Salmah Anim Zainuddin, Ahmad Anwar Mohd Khairuddin, Ismail Abd Mutalib, Mohd Azri Department of Computer Science International Islamic University Malaysia Kuala Lumpur Malaysia Department of Mechatronics Engineering International Islamic University Malaysia Kuala Lumpur Malaysia Department of Rehabilitation Sultan Ahmad Shah Medical Centre Pahang Malaysia Faculty of Manufacturing & Mechatronics Engineering Technology Universiti Malaysia Pahang Pahang Pekan Malaysia Department of Machine Design SIRIM Berhad Selangor Malaysia

ISBN: (纸本)9789819988181

In rehabilitation and medical offices, mechanomyography (MMG) is a noninvasive, painless technology that can be applied for a number of goals. The goal of this study is to present a thorough overview of recent studies on mechanomyography-based human limb rehabilitation. The present study illuminates the utilization of distinct transducers, including accelerometers, piezoelectric contact sensors, and condenser microphone sensors. Furthermore, it underscores the diverse results that these investigations have yielded. The main findings of this review, which apply to all of these forms of mechanomyography sensors, are that the ratio of sensor mass to muscle mass under observation is the most crucial factor in sensor selection. Therefore, it is believed that accelerometers are the most trustworthy devices for spotting MMG signals during both voluntary and induced muscular contractions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Accelerometers

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FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures 23

FlexiFed: Personalized Federated Learning for Edge Clients w...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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Task-Space Riccati Feedback based Whole Body Control for Underactuated Legged Locomotion

arXiv

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arXiv 2024年

作者： Yang, Shunpeng Hong, Zejun Li, Sen Wensing, Patrick Zhang, Wei Chen, Hua School of System Design and Intelligent Manufacturing Southern University of Science and Technology China Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology China Department of Aerospace and Mechanical Engineering University of Notre Dame United States

This manuscript primarily aims to enhance the performance of whole-body controllers(WBC) for underactuated legged locomotion. We introduce a systematic parameter design mechanism for the floating-base feedback control within the WBC. The proposed approach involves utilizing the linearized model of unactuated dynamics to formulate a Linear Quadratic Regulator(LQR) and solving a Riccati gain while accounting for potential physical constraints through a second-order approximation of the log-barrier function. And then the user-tuned feedback gain for the floating base task is replaced by a new one constructed from the solved Riccati gain. Extensive simulations conducted in MuJoCo with a point bipedal robot, as well as real-world experiments performed on a quadruped robot, demonstrate the effectiveness of the proposed method. In the different bipedal locomotion tasks, compared with the user-tuned method, the proposed approach is at least 12% better and up to 50% better at linear velocity tracking, and at least 7% better and up to 47% better at angular velocity tracking. In the quadruped experiment, linear velocity tracking is improved by at least 3% and angular velocity tracking is improved by at least 23% using the proposed method. © 2024, CC BY-NC-SA.

关键词： Angular velocity

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Pixel-Level Sea Ice Concentration Retrieval from Sentinel-1 SAR Imagery and Ancillary Data via Regional Loss Representations

Pixel-Level Sea Ice Concentration Retrieval from Sentinel-1 ...

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OCEANS

作者： Xinwei Chen Muhammed Patel Linlin Xu Yuhao Chen David A. Clausi K. Andrea Scott Weimin Huang School of Marine Science and Engineering South China University of Technology Department of System Design Engineering Vision and Image Processing Lab University of Waterloo Department of Mechanical & Mechatronics Engineering University of Waterloo Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's NL Canada

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

The automated monitoring and high resolution mapping of Arctic sea ice concentration (SIC) from synthetic aperture radar (SAR) images are crucial for various purposes, such as tactical navigation. However, the available labels for training deep learning-based SIC estimation models are mostly in coarse spatial resolution. This study tackles this key issue by proposing a novel loss calculation algorithm during model training. Consequently, a U-Net-based model can produce fine-grained SIC estimates via direct learning from ice charts in low resolution. The proposed method is evaluated with the recently release AI4Arctic Sea Ice Challenge Dataset with more than 500 Sentinel-1 SAR imager and associated ice charts. Visual interpretation and numerical results demonstrate the outstanding performance of the proposed method in terms of SIC mapping resolution. The effectiveness of incorporating multi-source ancillary data in performance improvement is also validated.

关键词： Training Visualization Sentinel-1 Estimation Radar polarimetry Numerical models Spatial resolution

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