目的研究视觉对人体姿势控制影响及其脑功能网络连接机制。方法以15名健康青年为研究对象,要求受试者分别进行30 s睁眼、闭眼的双腿站立平衡,采集平衡过程中身体压力中心(center of pressure,COP)和脑电。对COP进行样本熵(SampleEn)计算...目的研究视觉对人体姿势控制影响及其脑功能网络连接机制。方法以15名健康青年为研究对象,要求受试者分别进行30 s睁眼、闭眼的双腿站立平衡,采集平衡过程中身体压力中心(center of pressure,COP)和脑电。对COP进行样本熵(SampleEn)计算;对脑电θ、α和β频段,计算相位滞后指数(phase lag index,PLI)构建大脑功能网络,并基于图论计算集聚系数(C)、特征路径长度(L)及小世界网络属性(σ)。结果人体在双腿站立平衡过程中,闭眼COPY样本熵显著高于睁眼(P<0.05)。闭眼α频段PLI平均值显著高于睁眼(P<0.05);闭眼α频段C、σ显著高于睁眼,L显著低于睁眼(P<0.05)。闭眼时α频段额区-中央区-顶区之间的网络连接以及中央区和顶区内连接强度显著高于睁眼(P<0.05)。闭眼时α频段PLI平均值以及C值与COPY样本熵中度呈中度负相关(P<0.05)。睁眼时左前额区、左顶区、左枕区α频段PLI平均值与COPY样本熵呈中度负相关;闭眼时左中央区、右枕区α频段PLI平均值则与COPY样本熵呈中度负相关。结论人体在站立平衡时,当没有视觉信息输入时,身体平衡稳定性下降,同时伴随着脑电α频段的脑网络连接增强以及大脑处理信息的效率需提升。人体在不同的视觉条件下进行姿势控制时,大脑会采用不同的神经策略。展开更多
The development and the revolution of nanotechnology require more and effective methods to accurately estimating the timing analysis for any CMOS transistor level circuit. Many researches attempted to resolve the timi...The development and the revolution of nanotechnology require more and effective methods to accurately estimating the timing analysis for any CMOS transistor level circuit. Many researches attempted to resolve the timing analysis, but the best method found till the moment is the Static Timing Analysis (STA). It is considered the best solution because of its accuracy and fast run time. Transistor level models are mandatory required for the best estimating methods, since these take into consideration all analysis scenarios to overcome problems of multiple-input switching, false paths and high stacks that are found in classic CMOS gates. In this paper, transistor level graph model is proposed to describe the behavior of CMOS circuits under predictive Nanotechnology SPICE parameters. This model represents the transistor in the CMOS circuit as nodes in the graph regardless of its positions in the gates to accurately estimating the timing analysis rather than inaccurate estimating which caused by the false paths at the gate level. Accurate static timing analysis is estimated using the model proposed in this paper. Building on the proposed model and the graph theory concepts, new algorithms are proposed and simulated to compute transistor timing analysis using RC model. Simulation results show the validity of the proposed graph model and its algorithms by using predictive Nano-Technology SPICE parameters for the tested technology. An important and effective extension has been achieved in this paper for a one that was published in international conference.展开更多
随着电子病历(EHR)的广泛应用,基于深度学习的临床健康事件预测引起了众多研究者的关注。现有工作主要集中在挖掘患者的高阶时间特征,未能有效地学习疾病之间的隐关系。针对疾病表征学习的问题,本文提出一种新的疾病表示模型(Health Eve...随着电子病历(EHR)的广泛应用,基于深度学习的临床健康事件预测引起了众多研究者的关注。现有工作主要集中在挖掘患者的高阶时间特征,未能有效地学习疾病之间的隐关系。针对疾病表征学习的问题,本文提出一种新的疾病表示模型(Health Event Prediction Model Based on Dynamic and Static Features of Graph Nodes,DuDas)。该模型最终挖掘出的疾病隐表征包含静态和动态信息,最终实现对临床任务的预测。首先根据疾病共现频率构建疾病关系图,并通过one-hot编码模块为每个疾病节点分配一个初始隐表征。然后根据静态挖掘模块挖掘疾病的静态表征,并与相应的初始隐表征融合为初始动态隐表征。根据图卷积模块挖掘疾病之间的动态关系,学习疾病节点的最终动态隐表征。由于患者的就诊记录具有时间性,本文使用门控循环单元来挖掘历史诊断信息与当前诊断信息之间的关系。为了验证本文提出的方法的有效性,在2个真实数据集上进行实验。实验结果表明,本文提出的模型在预测健康事件任务上达到了更高水平。展开更多
文摘目的研究视觉对人体姿势控制影响及其脑功能网络连接机制。方法以15名健康青年为研究对象,要求受试者分别进行30 s睁眼、闭眼的双腿站立平衡,采集平衡过程中身体压力中心(center of pressure,COP)和脑电。对COP进行样本熵(SampleEn)计算;对脑电θ、α和β频段,计算相位滞后指数(phase lag index,PLI)构建大脑功能网络,并基于图论计算集聚系数(C)、特征路径长度(L)及小世界网络属性(σ)。结果人体在双腿站立平衡过程中,闭眼COPY样本熵显著高于睁眼(P<0.05)。闭眼α频段PLI平均值显著高于睁眼(P<0.05);闭眼α频段C、σ显著高于睁眼,L显著低于睁眼(P<0.05)。闭眼时α频段额区-中央区-顶区之间的网络连接以及中央区和顶区内连接强度显著高于睁眼(P<0.05)。闭眼时α频段PLI平均值以及C值与COPY样本熵中度呈中度负相关(P<0.05)。睁眼时左前额区、左顶区、左枕区α频段PLI平均值与COPY样本熵呈中度负相关;闭眼时左中央区、右枕区α频段PLI平均值则与COPY样本熵呈中度负相关。结论人体在站立平衡时,当没有视觉信息输入时,身体平衡稳定性下降,同时伴随着脑电α频段的脑网络连接增强以及大脑处理信息的效率需提升。人体在不同的视觉条件下进行姿势控制时,大脑会采用不同的神经策略。
文摘The development and the revolution of nanotechnology require more and effective methods to accurately estimating the timing analysis for any CMOS transistor level circuit. Many researches attempted to resolve the timing analysis, but the best method found till the moment is the Static Timing Analysis (STA). It is considered the best solution because of its accuracy and fast run time. Transistor level models are mandatory required for the best estimating methods, since these take into consideration all analysis scenarios to overcome problems of multiple-input switching, false paths and high stacks that are found in classic CMOS gates. In this paper, transistor level graph model is proposed to describe the behavior of CMOS circuits under predictive Nanotechnology SPICE parameters. This model represents the transistor in the CMOS circuit as nodes in the graph regardless of its positions in the gates to accurately estimating the timing analysis rather than inaccurate estimating which caused by the false paths at the gate level. Accurate static timing analysis is estimated using the model proposed in this paper. Building on the proposed model and the graph theory concepts, new algorithms are proposed and simulated to compute transistor timing analysis using RC model. Simulation results show the validity of the proposed graph model and its algorithms by using predictive Nano-Technology SPICE parameters for the tested technology. An important and effective extension has been achieved in this paper for a one that was published in international conference.
文摘随着电子病历(EHR)的广泛应用,基于深度学习的临床健康事件预测引起了众多研究者的关注。现有工作主要集中在挖掘患者的高阶时间特征,未能有效地学习疾病之间的隐关系。针对疾病表征学习的问题,本文提出一种新的疾病表示模型(Health Event Prediction Model Based on Dynamic and Static Features of Graph Nodes,DuDas)。该模型最终挖掘出的疾病隐表征包含静态和动态信息,最终实现对临床任务的预测。首先根据疾病共现频率构建疾病关系图,并通过one-hot编码模块为每个疾病节点分配一个初始隐表征。然后根据静态挖掘模块挖掘疾病的静态表征,并与相应的初始隐表征融合为初始动态隐表征。根据图卷积模块挖掘疾病之间的动态关系,学习疾病节点的最终动态隐表征。由于患者的就诊记录具有时间性,本文使用门控循环单元来挖掘历史诊断信息与当前诊断信息之间的关系。为了验证本文提出的方法的有效性,在2个真实数据集上进行实验。实验结果表明,本文提出的模型在预测健康事件任务上达到了更高水平。
