The tide plays a pivotal role in the ocean,affecting the global ocean circulation and supplying the bulk of the energy for the global meridional overturning circulation.To further investigate internal tides and their ...The tide plays a pivotal role in the ocean,affecting the global ocean circulation and supplying the bulk of the energy for the global meridional overturning circulation.To further investigate internal tides and their impacts on circulation,it is imperative to incorporate tidal forcing into the eddy-resolving global ocean circulation model.In this study,we successfully incorporated explicit tides(eight major constituents)into a global eddy-resolving general ocean circulation model and evaluated its tidal simulation ability.We obtained harmonic constants by analyzing sea surface height through tidal harmonic analysis and compared them with the analysis data Topex Poseidon Cross-Overs v9(TPXO9),the open ocean tide dataset from 102 open-ocean tide observations,and tide gauge stations from World Ocean Circulation Experiment.The results demonstrated that the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics/Institute of Atmospheric Physics(LASG/IAP)Climate System Ocean Model 3.0(LICOM3.0)effectively simulated tides,with errors predominantly occurring in nearshore regions.The tidal amplitude simulated in LICOM3.0 was greater than that of TPXO9,and these high-amplitude areas exhibited greater errors.The amplitude error of the M_(2) constituent was larger,while the phase error of the K_(1) constituent was more significant.Furthermore,we further compared our results with those from other models.展开更多
针对金刚石滚轮是一种回转体零件以及其加工制造过程中信息化集成程度低等特点,对金刚石滚轮的特征信息提取、特征加工方案决策、数控程序后置处理等关键技术进行了研究,采用了产品模型数据交换标准STEP AP 242,实现对金刚石滚轮的制造...针对金刚石滚轮是一种回转体零件以及其加工制造过程中信息化集成程度低等特点,对金刚石滚轮的特征信息提取、特征加工方案决策、数控程序后置处理等关键技术进行了研究,采用了产品模型数据交换标准STEP AP 242,实现对金刚石滚轮的制造特征信息提取,使用STEP AP 242和PDM相结合的集成方式,将金刚石滚轮三维模型作为制造加工信息的载体,并选择SolidWorks作为CAD和CAM软件,以及选择Aras Innovator作为PDM平台,使用C#编程语言和数据库技术开发了金刚石滚轮CAD/CAPP/CAM/PDM集成系统,并进行了实例验证。展开更多
In order to study the viscoelastic characteristics of asphalt mixtures in the whole-frequency range, a dynamic shearing rheometer is used to carry out dynamic frequency sweeps for asphalt mixtures, which obtains the t...In order to study the viscoelastic characteristics of asphalt mixtures in the whole-frequency range, a dynamic shearing rheometer is used to carry out dynamic frequency sweeps for asphalt mixtures, which obtains the three-dimensional relationships among G^＊, temperature and frequency. Then the time-temperature shift principle is used to translate the relationships from three dimensions to two dimensions, so master curves are obtained regarding dynamic modules changing along with the frequency over 15 amount levels. The Christensen- Anderson-Marasteanu （CAM） model, a kind of the rheological model, is used to analyze and compare rheological performances of several asphalt mixtures based on the obtained master curves. The results indicate that the dynamic theological test is an effective method for obtaining the master curve. Besides, the CAM model can describe well the viscoelastic deformation characteristics of asphalt mixtures.展开更多
在软土地区深基坑工程中,选取合适的土体本构模型并确定参数,对基坑开挖中的变形预测及支护结构优化设计尤为重要。为解决软土地区基坑工程中修正剑桥模型参数难以准确获取的问题,提出基于室内试验-贝叶斯理论的建筑基坑软土参数反演方...在软土地区深基坑工程中,选取合适的土体本构模型并确定参数,对基坑开挖中的变形预测及支护结构优化设计尤为重要。为解决软土地区基坑工程中修正剑桥模型参数难以准确获取的问题,提出基于室内试验-贝叶斯理论的建筑基坑软土参数反演方法。首先,通过室内三轴固结不排水试验及标准固结-回弹试验,获取2种土层的修正剑桥模型参数的试验取值及参数反演区间。其次,利用PLAXIS 3D建立基坑有限元模型,运用极差分析法对2种土层的修正剑桥模型参数进行敏感性分析,得出围护结构侧移、地表沉降和坑底隆起对模型参数的敏感性排序。最后,构建基于现场实测数据的贝叶斯反分析框架,利用马尔科夫蒙特卡洛(Markov Chain Monte Carlo,MCMC)算法获取修正剑桥模型参数在不同开挖阶段的后验分布。研究结果表明,贝叶斯反分析方法可用于修正剑桥模型的参数更新,参数更新后的相对误差与其敏感度相关,敏感度越高的参数更新后误差越大。采用更新后的参数计算基坑开挖引起的围护结构水平位移,并与实际监测值进行对比,计算误差相较于利用室内试验参数进行计算的结果显著减小,说明参数更新能提高反分析参数的准确性,验证了贝叶斯反演方法的可靠性和准确性。研究结果可为基坑工程的设计和施工提供理论依据和技术支持。展开更多
In neuropathological diseases such as Alzheimer's Disease(AD),neuroimaging and Magnetic Resonance Imaging(MRI)play crucial roles in the realm of Artificial Intelligence of Medical Things(AIoMT)by leveraging edge i...In neuropathological diseases such as Alzheimer's Disease(AD),neuroimaging and Magnetic Resonance Imaging(MRI)play crucial roles in the realm of Artificial Intelligence of Medical Things(AIoMT)by leveraging edge intelligence resources.However,accurately classifying MRI scans based on neurodegenerative diseases faces challenges due to significant variability across classes and limited intra-class differences.To address this challenge,we propose a novel approach aimed at improving the early detection of AD through MRI imaging.This method integrates a Convolutional Neural Network(CNN)with a Cascade Attention Model(CAM-CNN).The CAM-CNN model outperforms traditional CNNs in AD classification accuracy and processing complexity.In this architecture,the attention mechanism is effectively implemented by utilizing two constraint cost functions and a cross-network with diverse pre-trained parameters for a two-stream architecture.Additionally,two new cost functions,Satisfied Rank Loss(SRL)and Cross-Network Similarity Loss(CNSL),are introduced to enhance collaboration and overall network performance.Finally,a unique entropy addition method is employed in the attention module for network integration,converting intermediate outcomes into the final prediction.These components are designed to work collaboratively and can be sequentially trained for optimal performance,thereby enhancing the effectiveness of AD stage classification and robustness to interference from MR images.Validation using the Kaggle dataset demonstrates the model's accuracy of 99.07%in multiclass classification,ensuring precise classification and early detection of all AD subtypes.Further validation across three feature categories with varying numbers confirms the robustness of the proposed approach,with deviations from the standard criteria of less than 1%.Applied in Alzheimer's patient care,this capability holds promise for enhancing value-based therapy and clinical decision-making.It aids in differentiating Alzheimer's patients from healthy individuals,thereby improving patient care and enabling more targeted therapies.展开更多
基金The National Natural Science Foundation of China under contract Nos 41931182,42090040,42176024,and 42206006the National Key Program for Developing Basic Sciences under contract No.2022YFC3104802.
文摘The tide plays a pivotal role in the ocean,affecting the global ocean circulation and supplying the bulk of the energy for the global meridional overturning circulation.To further investigate internal tides and their impacts on circulation,it is imperative to incorporate tidal forcing into the eddy-resolving global ocean circulation model.In this study,we successfully incorporated explicit tides(eight major constituents)into a global eddy-resolving general ocean circulation model and evaluated its tidal simulation ability.We obtained harmonic constants by analyzing sea surface height through tidal harmonic analysis and compared them with the analysis data Topex Poseidon Cross-Overs v9(TPXO9),the open ocean tide dataset from 102 open-ocean tide observations,and tide gauge stations from World Ocean Circulation Experiment.The results demonstrated that the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics/Institute of Atmospheric Physics(LASG/IAP)Climate System Ocean Model 3.0(LICOM3.0)effectively simulated tides,with errors predominantly occurring in nearshore regions.The tidal amplitude simulated in LICOM3.0 was greater than that of TPXO9,and these high-amplitude areas exhibited greater errors.The amplitude error of the M_(2) constituent was larger,while the phase error of the K_(1) constituent was more significant.Furthermore,we further compared our results with those from other models.
文摘针对金刚石滚轮是一种回转体零件以及其加工制造过程中信息化集成程度低等特点,对金刚石滚轮的特征信息提取、特征加工方案决策、数控程序后置处理等关键技术进行了研究,采用了产品模型数据交换标准STEP AP 242,实现对金刚石滚轮的制造特征信息提取,使用STEP AP 242和PDM相结合的集成方式,将金刚石滚轮三维模型作为制造加工信息的载体,并选择SolidWorks作为CAD和CAM软件,以及选择Aras Innovator作为PDM平台,使用C#编程语言和数据库技术开发了金刚石滚轮CAD/CAPP/CAM/PDM集成系统,并进行了实例验证。
基金The National Natural Science Foundation of China (No.50278037)
文摘In order to study the viscoelastic characteristics of asphalt mixtures in the whole-frequency range, a dynamic shearing rheometer is used to carry out dynamic frequency sweeps for asphalt mixtures, which obtains the three-dimensional relationships among G^＊, temperature and frequency. Then the time-temperature shift principle is used to translate the relationships from three dimensions to two dimensions, so master curves are obtained regarding dynamic modules changing along with the frequency over 15 amount levels. The Christensen- Anderson-Marasteanu （CAM） model, a kind of the rheological model, is used to analyze and compare rheological performances of several asphalt mixtures based on the obtained master curves. The results indicate that the dynamic theological test is an effective method for obtaining the master curve. Besides, the CAM model can describe well the viscoelastic deformation characteristics of asphalt mixtures.
文摘在软土地区深基坑工程中,选取合适的土体本构模型并确定参数,对基坑开挖中的变形预测及支护结构优化设计尤为重要。为解决软土地区基坑工程中修正剑桥模型参数难以准确获取的问题,提出基于室内试验-贝叶斯理论的建筑基坑软土参数反演方法。首先,通过室内三轴固结不排水试验及标准固结-回弹试验,获取2种土层的修正剑桥模型参数的试验取值及参数反演区间。其次,利用PLAXIS 3D建立基坑有限元模型,运用极差分析法对2种土层的修正剑桥模型参数进行敏感性分析,得出围护结构侧移、地表沉降和坑底隆起对模型参数的敏感性排序。最后,构建基于现场实测数据的贝叶斯反分析框架,利用马尔科夫蒙特卡洛(Markov Chain Monte Carlo,MCMC)算法获取修正剑桥模型参数在不同开挖阶段的后验分布。研究结果表明,贝叶斯反分析方法可用于修正剑桥模型的参数更新,参数更新后的相对误差与其敏感度相关,敏感度越高的参数更新后误差越大。采用更新后的参数计算基坑开挖引起的围护结构水平位移,并与实际监测值进行对比,计算误差相较于利用室内试验参数进行计算的结果显著减小,说明参数更新能提高反分析参数的准确性,验证了贝叶斯反演方法的可靠性和准确性。研究结果可为基坑工程的设计和施工提供理论依据和技术支持。
基金funded by the National Elites Foundation(No.711.5095).
文摘In neuropathological diseases such as Alzheimer's Disease(AD),neuroimaging and Magnetic Resonance Imaging(MRI)play crucial roles in the realm of Artificial Intelligence of Medical Things(AIoMT)by leveraging edge intelligence resources.However,accurately classifying MRI scans based on neurodegenerative diseases faces challenges due to significant variability across classes and limited intra-class differences.To address this challenge,we propose a novel approach aimed at improving the early detection of AD through MRI imaging.This method integrates a Convolutional Neural Network(CNN)with a Cascade Attention Model(CAM-CNN).The CAM-CNN model outperforms traditional CNNs in AD classification accuracy and processing complexity.In this architecture,the attention mechanism is effectively implemented by utilizing two constraint cost functions and a cross-network with diverse pre-trained parameters for a two-stream architecture.Additionally,two new cost functions,Satisfied Rank Loss(SRL)and Cross-Network Similarity Loss(CNSL),are introduced to enhance collaboration and overall network performance.Finally,a unique entropy addition method is employed in the attention module for network integration,converting intermediate outcomes into the final prediction.These components are designed to work collaboratively and can be sequentially trained for optimal performance,thereby enhancing the effectiveness of AD stage classification and robustness to interference from MR images.Validation using the Kaggle dataset demonstrates the model's accuracy of 99.07%in multiclass classification,ensuring precise classification and early detection of all AD subtypes.Further validation across three feature categories with varying numbers confirms the robustness of the proposed approach,with deviations from the standard criteria of less than 1%.Applied in Alzheimer's patient care,this capability holds promise for enhancing value-based therapy and clinical decision-making.It aids in differentiating Alzheimer's patients from healthy individuals,thereby improving patient care and enabling more targeted therapies.
