检索结果-南通市图书馆

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

National Science Review 2024年第2期11卷 231-263页

作者： Kyung Rok Pyun Kangkyu Kwon Myung Jin Yoo Kyun Kyu Kim Dohyeon Gong Woon-Hong Yeo Seungyong Han Seung Hwan Ko Department of Mechanical engineering Seoul National University IEN Center for Human-Centric Interfaces and engineering Institute for Electronics and NanotechnologyGeorgia Institute of Technology school of Electrical and Computer engineering Georgia Institute of Technology Department of Chemical engineering Stanford University Department of Mechanical engineering Ajou University George W.Woodruff school of Mechanical engineering Georgia Institute of Technology Institute of Advanced Machinery and Design(SNU-IAMD) Seoul National University

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human–machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

关键词： wearable sensor soft electronics artificial intelligence machine learning human–machine interfaces gesture recognition

Transparent micropatterned conductive films based on highlyordered nanowire network

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Nano Research 2024年第5期17卷 4288-4297页

作者： Xin-Ran Zhang Hai-Tao Deng Xu Zeng Yi-Lin Wang Peng Huang Xiao-Sheng Zhang school of Integrated Circuit Science and engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Transparent conductive films that are based on nanowire networks are essential to construct flexible,wearable,and even stretchable ***,large-scale precise micropatterning,especially with regard to the controllability of the organizing orientation of nanowires,is a critical ***,we proposed a liquid film rupture self-assembly approach for manufacturing transparent conductive films with microstructure arrays based on a highly ordered nanowire *** large-scale microstructure conductive films were fabricated through air-liquid interface self-assembly and liquid film rupture *** typical micropattern morphologies,including square,hexagon,circle,serpentine,etc.,were prepared to reveal the universal applicability of the proposed *** homogeneity and controllability of this approach were verified for multiple *** the assembly cycles increasing,the optical transmittance decreases *** addition,theoretical model analysis is carried out,and the analytical formula of the speed of the film moving with the surface tension and the density of the liquid film is ***,the feasibility of this approach for piezoresistive strain sensors is *** fabrication approach demonstrated a cost-effective and efficient method for precisely arranging nanowires,which is useful in transparent and wearable applications.

关键词： flexible electronics transparent conductive network micropatterning nanowires micro electromechanical systems(MEMS)

Single-structure 3-axis Lorentz force magnetometer based on an AlN-on-Si MEMS resonator

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Microsystems & Nanoengineering 2024年第3期10卷 75-86页

作者： Cheng Tu Xu-heng Ou-Yang Ying-jie Wu Xiao-sheng Zhang school of Integrated Circuit Science and engineering University of Electronic Science and Technology of ChinaChengdu 611731China

This work presents a single-structure 3-axis Lorentz force magnetometer(LFM)based on an AlN-on-Si MEMS *** operation of the proposed LFM relies on the flexible manipulation of applied excitation currents in different directions and frequencies,enabling the effective actuation of two mechanical vibration modes in a single device for magnetic field measurements in three ***,the excited out-of-plane drum-like mode at 277 kHz is used for measuring the x-and y-axis magnetic fields,while the in-plane square-extensional mode at 5.4 MHz is used for measuring the z-axis magnetic *** different configurations of applied excitation currents ensure good crossinterference immunity among the three *** to conventional capacitive LFMs,the proposed piezoelectric LFM utilizes strong electromechanical coupling from the AlN layer,which allows it to operate at ambient pressure with a high *** understand and analyze the measured results,a novel equivalent circuit model for the proposed LFM is also reported in this work,which serves to separate the effect of Lorentz force from the unwanted capacitive *** demonstrated 3-axis LFM exhibits measured magnetic responsivities of 1.74 ppm/mT,1.83 ppm/mT and 6.75 ppm/mT in the x-,y-and z-axes,respectively,which are comparable to their capacitive counterparts.

关键词： resonator Lorentz structure

Utilizing interpretable stacking ensemble learning and NSGA-Ⅲ for the prediction and optimisation of building photo-thermal environment and energy consumption

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Building Simulation 2024年第5期17卷 819-838页

作者： Yeqin Shen Yubing Hu Kai Cheng Hainan Yan Kaixiang Cai Jianye Hua Xuemin Fei Qinyu Wang school of Arts Jiangsu UniversityZhenjiang212013China school of Architecture and Urban Planning Nanjing UniversityNanjing210093China school of Computer Science and Communication engineering Jiangsu UniversityZhenjiang212013China

This study develops an approach consisting of a stacking model integrated with a multi-objective optimisation algorithm aimed at predicting and optimising the ecological performance of *** integrated model consists of five base models and a meta-model,which significantly improves the prediction ***,the R2 value was improved by 9.19% and the error metrics MAE,MSE,MAPE,and CVRMSE were reduced by 69.47%,79.88%,67.32%,and 57.02%,respectively,compared to the single prediction *** to the research on interpretable machine learning,adding the SHAP value gives us a deeper understanding of the impact of each architectural design parameter on the *** the multi-objective optimisation part,we used the NSGA-Ⅲ algorithm to successfully improve the energy efficiency,daylight utilisation and thermal comfort of the ***,the optimal design solution reduces the energy use intensity by 31.6 kWh/m^(2),improves the useful daylight index by 39%,and modulated the thermal comfort index,resulting in a decrement of 0.69℃ for the summer season and an enhancement of 0.64℃ for the winter season,***,this study provides building designers and decision makers with a tool to make better design decisions at an early stage to achieve a better combination of energy efficiency,daylight utilisation and thermal comfort optimisation in an integrated manner,providing an important support for achieving sustainable building design.

关键词： building ecological performance ensemble learning multi-objective optimisation sustainable design explainable machine learning

Integrating BIM and machine learning to predict carbon emissions under foundation materialization stage:Case study of China’s 35 public buildings

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Frontiers of architectural Research 2024年第4期13卷 876-894页

作者： Haining Wang Yue Wang Liang Zhao Wei Wang Zhixing Luo Zixiao Wang Jinghui Luo Yihan Lv school of Architecture Southeast UniversityNanjing 210096China Key Laboratory of Urban and architectural Heritage Conservation(Southeast University) Ministry of EducationNanjing 210096China school of Architecture Xi’an University of Architecture and TechnologyXi’an 710055China Hubei Haizheng Technology Co. LtdWuhan 430000China school of Energy and Environment engineering Hebei University of EngineeringHandan 056038China State Key Laboratory of Green Building Xi’an University of Architecture and TechnologyXi’an 710055China

For the significant energy consumption and environmental impact,it is crucial to identify the carbon emission characteristics of building foundations construction during the design *** study would like to establish a process-based carbon evaluating model,by adopting Building Information Modeling(BIM),and calculated the materialization-stage carbon emissions of building foundations without basement space in China,and identifying factors influencing the emissions through correlation *** five factors include the building function type,building structure type,foundation area,foundation treatment method,and foundation ***,this study develops several machine learning-based predictive models,including Decision Tree,Random Forest,XGBoost,and Neural *** these models,XGBoost demonstrates a relatively higher degree of accuracy and minimal errors,can achieve the RMSE of 206.62 and R2 of 0.88 based on testing group *** study reveals a substantial variability carbon emissions per building’s floor area of foundations,ranging from 100 to 2000 kgCO_(2)e/m^(2),demonstrating the potential for optimizing carbon emissions during the design phase of ***,materials contribute significantly to total carbon emissions,accounting for 78%e97%,suggesting a significant opportunity for using BIM technology in the design phase to optimize carbon reduction efforts.

关键词： Building foundations Carbon emissions Building information modeling Machine learning Sustainable architectural design

Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities:Current trends and future outlook

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Building Simulation 2024年第5期17卷 695-722页

作者： Nilabhra Mondal Prashant Anand Ansar Khan Chirag Deb David Cheong Chandra Sekhar Dev Niyogi Mattheos Santamouris Department of Architecture and Regional Planning Indian Institute of Technology KharagpurKharagpurIndia Department of Geography Lalbaba CollegeUniversity of CalcuttaKolkataIndia school of Architecture Design and PlanningThe University of SydneyAustralia Department of the Built Environment National University of SingaporeSingapore Department of Geological Sciences Jackson School of GeosciencesThe University of Texas at AustinAustinUSA DepartmentofCivil Architecturaland Environmental EngineeringThe Universityof Texas at AustinAustinUSA Faculty of Built Environment University of New South WalesAustralia

Energy demand fluctuations due to low probability high impact(LPHI)micro-climatic events such as urban heat island effect(UHI)and heatwaves,pose significant challenges for urban infrastructure,particularly within urban *** short term load forecasting(STLF)of buildings in urban micro-climatic setting(UMS)is obscured by the complex interplay of surrounding morphology,micro-climate and inter-building energy *** urban building energy modelling(UBEM)approaches to provide quantitative insights about building energy consumption often neglect the synergistic impacts of micro-climate and urban morphology in short temporal *** order modelling,unavailability of rich urban datasets such as building key performance indicators for building archetypes-characterization,limit the inter-building energy dynamics consideration into *** addition,mismatch of resolutions of spatio-temporal datasets(meso to micro scale transition),LPHI events extent prediction around UMS as well as its accurate quantitative inclusion in UBEM input organization step pose another degree of *** review aims to direct attention towards an integrated-UBEM(i-UBEM)framework to capture the building load fluctuation over multi-scale spatio–temporal *** highlights usage of emerging data-driven hybrid approaches,after systematically analysing developments and limitations of recent physical,data-driven artificial intelligence and machine learning(AI-ML)based modelling *** also discusses the potential integration of google earth engine(GEE)-cloud computing platform in UBEM input organization step to(i)map the land surface temperature(LST)data(quantitative attribute implying LPHI event occurrence),(ii)manage and pre-process high-resolution spatio-temporal UBEM *** the potential of digital twin,central structed data models to integrate along UBEM workflow to reduce uncertainties related to building archetype characterizat

关键词： urban heat island(UHI) urbanmicro-climate urban morphology urban building energy modelling(UBEM) digitaltwin shorttermload forecasting(STLF) googleearthengine(GEE)

A Symmetric-Actuating Linear Piezoceramic Ultrasonic Motor Capable of Producing a Scissoring Effect

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Research 2024年第1期2023卷 571-581页

作者： Zhanmiao Li Xingyu Yi Rongqi Zhu Zhonghui Yu Xiaoting Yuan MohammadJavad PourhosseiniAsl Shuxiang Dong school of Materials Science and engineering Peking UniversityBeijing 100871China College of Mechatronics and Control engineering and Institute for Advanced StudyShenzhen UniversityShenzhen 518051China

Conventionally,to produce a linear motion,one motor’s stator is employed to drive one runner moving forward or *** far,there is almost no report of one electromechanical motor or piezoelectric ultrasonic motor that can directly generate two symmetrical linear motions,while this function is desired for precise scissoring and grasping in the minimally invasive surgery ***,we report a brandnew symmetric-actuating linear piezoceramic ultrasonic motor capable of generating symmetrical linear motions of two outputs directly without additional mechanical transmission *** key component of the motor is an(2×3)arrayed piezoceramic bar stator operating in the coupled resonant mode of the first longitudinal(L_(1))and third bending(B_(3))modes,leading to symmetric elliptical vibration trajectories at its two ends.A pair of microsurgical scissors is used as the end-effector,demonstrating a very promising future for high-precision microsurgical operations.

关键词： field symmetric backward

Composite Braking Control Strategy for electromechanical-Hydraulic Vehicles

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

CSEE Journal of Power and Energy Systems 2024年第4期10卷 1722-1732页

作者： Zewen Meng Jichao Hong Hongxin Zhang Tiezhu Zhang Xiaoming Xu Qinghai Zhao Jian Yang College of Mechanical and Electrical engineering Qingdao UniversityQingdao 260071China Power Integration and Energy Storage Systems engineering Technology Center(Qingdao) Qingdao 260071China school of Mechanical engineering University of Science and Technology BeijingBeijing 100083China Shunde Graduate school of University of Science and Technology Beijing Foshan 528000China

Short driving ranges and low braking energy recovery efficiencies are two recognized technical bottlenecks to be overcome in electric vehicles. In this paper, a novel electromechanical-hydraulic coupling system is proposed and integrated as a powertrain for electric vehicles, which can assist the electric vehicle to fully utilize its braking energy. The hydraulic regenerative braking force and electric regenerative braking force can provide all the braking needs using the medium and small braking intensities. Furthermore, an improved compound brake control strategy based on the braking force distribution is proposed and simulated. The results show that under the premise of ensuring braking stability, the electromechanical-hydraulic coupling driving electric vehicle can adapt to various working conditions with excellent energy-saving results. The hydraulic accumulator recovery efficiency is above 99%, and the state of charge consumption rate of the battery pack can be reduced by more than 9%. More importantly, the proposed hybrid power system can significantly improve the driving range and energy efficiency, as well as reduce the consumers' mileage anxiety in electric vehicles.

关键词： Braking control strategy electric vehicle electromechanical-hydraulic coupling energy recovery state of charge

Uncertainty-Aware Physical Simulation of Neural Radiance Fields for Fluids

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in engineering & Sciences 2024年第7期140卷 1143-1163页

作者： Haojie Lian Jiaqi Wang Leilei Chen Shengze Li Ruochen Cao Qingyuan Hu Peiyun Zhao Key Laboratory of In-Situ Property-Improving Mining of Ministry of Education Taiyuan University of TechnologyTaiyuan030024China Henan International Joint Laboratory of Structural Mechanics and Computational Simulation College of Architectural and Civil EngineeringHuanghuai UniversityZhumadian463000China National Innovation Institute of Defense Technology Academy of Military ScienceBeijing100091China school of Software Taiyuan University of TechnologyJinzhong030600China school of Science Jiangnan UniversityWuxi214122China

This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke *** approach reconstructs color and density fields from 2D images using Neural Radiance Field(NeRF)and improves image quality using frequency *** NeRF model is obtained via joint training ofmultiple artificial neural networks,whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each *** addition,customized physics-informed neural network(PINN)with residual blocks and two-layer activation functions are utilized to input the density fields of the NeRF into Navier-Stokes equations and convection-diffusion equations to reconstruct the velocity *** velocity uncertainties are also evaluated through ensemble *** effectiveness of the proposed algorithm is demonstrated through numerical *** presentmethod is an important step towards downstream tasks such as reliability analysis and robust optimization in engineering design.

关键词： Uncertainty quantification neural radiance field physics-informed neural network frequency regularization twolayer activation function ensemble learning