An initial value problem was considered for a coupled differential system with multi-term Caputo type fractional derivatives. By means of nonlinear alternative of Leray-Schauder and Banach contraction principle,the ex...An initial value problem was considered for a coupled differential system with multi-term Caputo type fractional derivatives. By means of nonlinear alternative of Leray-Schauder and Banach contraction principle,the existence and uniqueness of solutions for the system were derived. Using a fractional predictorcorrector method, a numerical method was presented for the specified system. An example was given to illustrate the obtained results.展开更多
In this paper, we introduce high-order finite volume methods for the multi-term time fractional sub-diffusion equation. The time fractional derivatives are described in Caputo’s sense. By using some operators, we obt...In this paper, we introduce high-order finite volume methods for the multi-term time fractional sub-diffusion equation. The time fractional derivatives are described in Caputo’s sense. By using some operators, we obtain the compact finite volume scheme have high order accuracy. We use a compact operator to deal with spatial direction;then we can get the compact finite volume scheme. It is proved that the finite volume scheme is unconditionally stable and convergent in L<sub>∞</sub>-norm. The convergence order is O(τ<sup>2-α</sup> + h<sup>4</sup>). Finally, two numerical examples are given to confirm the theoretical results. Some tables listed also can explain the stability and convergence of the scheme.展开更多
The acceleration of urbanization has led to an increase in the number of urban floating population, which leads to more demands for the housing rental market. With the support of policies, long-term lease apartments h...The acceleration of urbanization has led to an increase in the number of urban floating population, which leads to more demands for the housing rental market. With the support of policies, long-term lease apartments have begun to emerge. However, under the multi-subject supply, longterm lease apartments have encountered problems such as small profits in their development. Starting from the background of the development of long-term lease apartments, this study classified the main types of long-term lease apartments, analyzed the four profit models of comprehensive profit, expansion of rent difference, REITs and value-added services based on their business models, and proposed corresponding suggestions on the profitability of long-term lease apartments according to the current situation of profit difficulty of long-term lease apartments and the lack of profit models.展开更多
With the help of the extended Huygens-Fresnel principle and the short-term mutual coherence function, the analytical formula of short-term average intensity for multi-Gaussian beam （MGB） in the turbulent a^mosphere ...With the help of the extended Huygens-Fresnel principle and the short-term mutual coherence function, the analytical formula of short-term average intensity for multi-Gaussian beam （MGB） in the turbulent a^mosphere has been derived. The intensity in the absence of turbulence and the long-term average intensity in turbulence can both also be expressed in this formula. As special cases, comparisons among short-term average intensity, long-term average intensity, and the intensity in the absence of turbulence for flat topped beam and annular beam are carried out. The effects of the order of MGB, propagation distance and aperture radius on beam spreading are analysed and discussed in detail.展开更多
当前大语言模型的兴起为自然语言处理、搜索引擎、生命科学研究等领域的研究者提供了新思路,但大语言模型存在资源消耗高、推理速度慢,难以在工业场景尤其是垂直领域应用等方面的缺点。针对这一问题,提出了一种多尺度卷积神经网络(convo...当前大语言模型的兴起为自然语言处理、搜索引擎、生命科学研究等领域的研究者提供了新思路,但大语言模型存在资源消耗高、推理速度慢,难以在工业场景尤其是垂直领域应用等方面的缺点。针对这一问题,提出了一种多尺度卷积神经网络(convolutional neural network,CNN)与双向长短期记忆神经网络(long short term memory,LSTM)融合的唐卡问句分类模型,本文模型将数据的全局特征与局部特征进行融合实现唐卡问句分类任务,全局特征反映数据的本质特点,局部特征关注数据中易被忽视的部分,将二者以拼接的方式融合以丰富句子的特征表示。通过在Thangka数据集与THUCNews数据集上进行实验,结果表明,本文模型相较于Bert模型在精确度上略优,在训练时间上缩短了1/20,运算推理时间缩短了1/3。在公开数据集上的实验表明,本文模型在文本分类任务上也表现出了较好的适用性和有效性。展开更多
针对传统卷积神经网络故障诊断方法提取特征不丰富,容易丢失故障敏感信息,且在单一尺度处理方法限制实际复杂工况下故障特性的深度挖掘问题,提出了注意力机制的多尺度卷积神经网络和双向长短期记忆(bi-directional long short-term memo...针对传统卷积神经网络故障诊断方法提取特征不丰富,容易丢失故障敏感信息,且在单一尺度处理方法限制实际复杂工况下故障特性的深度挖掘问题,提出了注意力机制的多尺度卷积神经网络和双向长短期记忆(bi-directional long short-term memory,BiLSTM)网络融合的迁移学习故障诊断方法。该方法首先应用不同尺寸池化层和卷积核捕获振动信号的多尺度特征;然后引入多头自注意力机制自动地给予特征序列中的不同部分不同的权重,进一步加强特征表示的能力;其次利用BiLSTM结构引入双向性质提取特征前后之间的内部关系实现信息的逐层传递;最后利用多核最大均值差异减小源域和目标域在预训练模型中各层上的概率分布差异并利用少量标记的目标域数据再对模型进行训练。试验结果表明,所提方法在江南大学(JNU)、德国帕德博恩大学(PU)公开轴承数据集上平均准确率分别为98.43%和97.66%,该方法在重庆长江轴承股份有限公司自制的轴承故障数据集上也表现出了极高的准确率和较快的收敛速度,为有效诊断振动旋转部件故障提供了实际依据。展开更多
精准的分布式光伏短期发电功率预测有助于电力系统运行与功率就地平衡。该文提出一种基于BIRCH(balanced iterative reducing and clustering using hierarchies)相似日聚类的L-Transformer(LSTM-Transformer)模型进行短期光伏功率预测...精准的分布式光伏短期发电功率预测有助于电力系统运行与功率就地平衡。该文提出一种基于BIRCH(balanced iterative reducing and clustering using hierarchies)相似日聚类的L-Transformer(LSTM-Transformer)模型进行短期光伏功率预测。首先使用BIRCH无监督聚类算法对历史数据聚类得到3种典型天气,根据聚类结果划分测试集对模型进行训练。为提高不同天气类型下的预测精度,采用双层架构的L-Transformer模型,首层通过长短期记忆网络(long short term memory,LSTM)的门控单元机制捕捉时间序列中的长期依赖关系;次层结合Transformer模型的自注意力机制聚焦于当前任务更关键的特征量,通过多注意力头与光伏数据特征量相结合生成向量,注意力头并行计算,从而高效、精确地预测短期光伏功率。实测数据验证结果表明L-Transformer模型对于不同天气类型功率预测泛化性优异、精确度高,气象数据波动大时鲁棒性强。展开更多
The acute effect of acupuncture on Alzheimer＇s disease,i.e.,on brain activation during treatment,has been reported.However,the effect of long-term acupuncture on brain activation in Alzheimer＇s disease is unclear.Th...The acute effect of acupuncture on Alzheimer＇s disease,i.e.,on brain activation during treatment,has been reported.However,the effect of long-term acupuncture on brain activation in Alzheimer＇s disease is unclear.Therefore,in this study,we performed long-term needling at Zusanli（ST36）or a sham point（1.5 mm lateral to ST36）in a rat Alzheimer＇s disease model,for 30 minutes,once per day,for 30 days.The rats underwent 18F-fluorodeoxyglucose positron emission tomography scanning.Positron emission tomography images were processed with SPM2.The brain areas activated after needling at ST36 included the left hippocampus,the left orbital cortex,the left infralimbic cortex,the left olfactory cortex,the left cerebellum and the left pons.In the sham-point group,the activated regions were similar to those in the ST36 group.However,the ST36 group showed greater activation in the cerebellum and pons than the sham-point group.These findings suggest that long-term acupuncture treatment has targeted regulatory effects on multiple brain regions in rats with Alzheimer＇s disease.展开更多
基金National Natural Science Foundation of China(No.11371087)
文摘An initial value problem was considered for a coupled differential system with multi-term Caputo type fractional derivatives. By means of nonlinear alternative of Leray-Schauder and Banach contraction principle,the existence and uniqueness of solutions for the system were derived. Using a fractional predictorcorrector method, a numerical method was presented for the specified system. An example was given to illustrate the obtained results.
文摘In this paper, we introduce high-order finite volume methods for the multi-term time fractional sub-diffusion equation. The time fractional derivatives are described in Caputo’s sense. By using some operators, we obtain the compact finite volume scheme have high order accuracy. We use a compact operator to deal with spatial direction;then we can get the compact finite volume scheme. It is proved that the finite volume scheme is unconditionally stable and convergent in L<sub>∞</sub>-norm. The convergence order is O(τ<sup>2-α</sup> + h<sup>4</sup>). Finally, two numerical examples are given to confirm the theoretical results. Some tables listed also can explain the stability and convergence of the scheme.
文摘The acceleration of urbanization has led to an increase in the number of urban floating population, which leads to more demands for the housing rental market. With the support of policies, long-term lease apartments have begun to emerge. However, under the multi-subject supply, longterm lease apartments have encountered problems such as small profits in their development. Starting from the background of the development of long-term lease apartments, this study classified the main types of long-term lease apartments, analyzed the four profit models of comprehensive profit, expansion of rent difference, REITs and value-added services based on their business models, and proposed corresponding suggestions on the profitability of long-term lease apartments according to the current situation of profit difficulty of long-term lease apartments and the lack of profit models.
文摘With the help of the extended Huygens-Fresnel principle and the short-term mutual coherence function, the analytical formula of short-term average intensity for multi-Gaussian beam （MGB） in the turbulent a^mosphere has been derived. The intensity in the absence of turbulence and the long-term average intensity in turbulence can both also be expressed in this formula. As special cases, comparisons among short-term average intensity, long-term average intensity, and the intensity in the absence of turbulence for flat topped beam and annular beam are carried out. The effects of the order of MGB, propagation distance and aperture radius on beam spreading are analysed and discussed in detail.
文摘当前大语言模型的兴起为自然语言处理、搜索引擎、生命科学研究等领域的研究者提供了新思路,但大语言模型存在资源消耗高、推理速度慢,难以在工业场景尤其是垂直领域应用等方面的缺点。针对这一问题,提出了一种多尺度卷积神经网络(convolutional neural network,CNN)与双向长短期记忆神经网络(long short term memory,LSTM)融合的唐卡问句分类模型,本文模型将数据的全局特征与局部特征进行融合实现唐卡问句分类任务,全局特征反映数据的本质特点,局部特征关注数据中易被忽视的部分,将二者以拼接的方式融合以丰富句子的特征表示。通过在Thangka数据集与THUCNews数据集上进行实验,结果表明,本文模型相较于Bert模型在精确度上略优,在训练时间上缩短了1/20,运算推理时间缩短了1/3。在公开数据集上的实验表明,本文模型在文本分类任务上也表现出了较好的适用性和有效性。
文摘针对传统卷积神经网络故障诊断方法提取特征不丰富,容易丢失故障敏感信息,且在单一尺度处理方法限制实际复杂工况下故障特性的深度挖掘问题,提出了注意力机制的多尺度卷积神经网络和双向长短期记忆(bi-directional long short-term memory,BiLSTM)网络融合的迁移学习故障诊断方法。该方法首先应用不同尺寸池化层和卷积核捕获振动信号的多尺度特征;然后引入多头自注意力机制自动地给予特征序列中的不同部分不同的权重,进一步加强特征表示的能力;其次利用BiLSTM结构引入双向性质提取特征前后之间的内部关系实现信息的逐层传递;最后利用多核最大均值差异减小源域和目标域在预训练模型中各层上的概率分布差异并利用少量标记的目标域数据再对模型进行训练。试验结果表明,所提方法在江南大学(JNU)、德国帕德博恩大学(PU)公开轴承数据集上平均准确率分别为98.43%和97.66%,该方法在重庆长江轴承股份有限公司自制的轴承故障数据集上也表现出了极高的准确率和较快的收敛速度,为有效诊断振动旋转部件故障提供了实际依据。
文摘精准的分布式光伏短期发电功率预测有助于电力系统运行与功率就地平衡。该文提出一种基于BIRCH(balanced iterative reducing and clustering using hierarchies)相似日聚类的L-Transformer(LSTM-Transformer)模型进行短期光伏功率预测。首先使用BIRCH无监督聚类算法对历史数据聚类得到3种典型天气,根据聚类结果划分测试集对模型进行训练。为提高不同天气类型下的预测精度,采用双层架构的L-Transformer模型,首层通过长短期记忆网络(long short term memory,LSTM)的门控单元机制捕捉时间序列中的长期依赖关系;次层结合Transformer模型的自注意力机制聚焦于当前任务更关键的特征量,通过多注意力头与光伏数据特征量相结合生成向量,注意力头并行计算,从而高效、精确地预测短期光伏功率。实测数据验证结果表明L-Transformer模型对于不同天气类型功率预测泛化性优异、精确度高,气象数据波动大时鲁棒性强。
基金supported by the National Basic Research Program of China(973 Program),No.2006CB504505,2012CB518504the National Natural Science Foundation of China,No.90709027+1 种基金the Student's Platform for Innovation and Entrepreneurship Training Program of Southern Medical University of China,No.201512121165the Doctoral Foundation of Guangdong Medical University of China,No.2XB13058
文摘The acute effect of acupuncture on Alzheimer＇s disease,i.e.,on brain activation during treatment,has been reported.However,the effect of long-term acupuncture on brain activation in Alzheimer＇s disease is unclear.Therefore,in this study,we performed long-term needling at Zusanli（ST36）or a sham point（1.5 mm lateral to ST36）in a rat Alzheimer＇s disease model,for 30 minutes,once per day,for 30 days.The rats underwent 18F-fluorodeoxyglucose positron emission tomography scanning.Positron emission tomography images were processed with SPM2.The brain areas activated after needling at ST36 included the left hippocampus,the left orbital cortex,the left infralimbic cortex,the left olfactory cortex,the left cerebellum and the left pons.In the sham-point group,the activated regions were similar to those in the ST36 group.However,the ST36 group showed greater activation in the cerebellum and pons than the sham-point group.These findings suggest that long-term acupuncture treatment has targeted regulatory effects on multiple brain regions in rats with Alzheimer＇s disease.
