少样本学习是目前机器学习研究领域的热点和难点.针对现有的少样本学习模型不能有效捕捉数据特征与数据标签之间的联系,造成分类模型泛化能力弱的问题,提出一种基于元学习的原型空间图卷积网络少样本学习模型FSL-GCNPS(Few-Shot Learnin...少样本学习是目前机器学习研究领域的热点和难点.针对现有的少样本学习模型不能有效捕捉数据特征与数据标签之间的联系,造成分类模型泛化能力弱的问题,提出一种基于元学习的原型空间图卷积网络少样本学习模型FSL-GCNPS(Few-Shot Learning of Graph Convolutional Network on Prototype Space).首先,利用卷积神经网络提取多任务数据的特征向量;其次,为了将特征向量映射到原型空间中,根据元学习的训练策略得到特征向量的类原型表达;然后,通过类原型向量和类向量之间的嵌入表示,构建图结构数据,并进行图卷积网络训练、推理.实验结果表明,相较于经典少样本学习方法,FSL-GCNPS模型拥有更好的分类准确率和分类稳定性.同时,在医学图像领域数据集上实验表明,FSL-GCNPS具有很好的跨域适应性.展开更多
Microwave-assisted mechanical excavation has great application prospects in mines and tunnels,but there are few field experiments on microwave-assisted rock breaking.This paper takes the Sishanling iron mine as the re...Microwave-assisted mechanical excavation has great application prospects in mines and tunnels,but there are few field experiments on microwave-assisted rock breaking.This paper takes the Sishanling iron mine as the research object and adopts the self-developed high-power microwave-induced fracturing test system for hard rock to conduct field experiments of microwave-induced fracturing of iron ore.The heating and reflection evolution characteristics of ore under different microwave parameters(antenna type,power,and working distance)were studied,and the optimal microwave parameters were obtained.Subsequently,the ore was irradiated with the optimal microwave parameters,and the cracking effect of the ore under the action of the high-power open microwave was analyzed.The results show that the reflection coefficient(standing wave ratio)can be rapidly(<5 s)and automatically adjusted below the preset threshold value(1.6)as microwave irradiation is performed.When using a right-angle horn antenna with a working distance of 5 cm,the effect of automatic reflection adjustment reaches the best among other antenna types and working distances.When the working distance is the same,the average temperature of the irradiation surface and the area of the high-temperature area under the action of the two antennas(right-angled and equal-angled horn antenna)are basically the same and decrease with the increase of working distance.The optimal microwave parameters are:a right-angle horn antenna with a working distance of 5 cm.Subsequently,in further experiments,the optimal parameters were used to irradiate for 20 s and 40 s at a microwave power of 60 kW,respectively.The surface damage extended 38 cm×30 cm and 53 cm×30 cm,respectively,and the damage extended to a depth of about 50 cm.The drilling speed was increased by 56.2%and 66.5%,respectively,compared to the case when microwaves were not used.展开更多
方面级情感分类是一种细粒度的情感分析任务,旨在分类出文本中不同方面的情感。目前,现有方面级情感分类模型存在特征提取层次浅、泛化能力弱等问题。为此,该文提出一种基于融合对抗网络的方面级情感分类模型ASFAN(Aspect-level Sentime...方面级情感分类是一种细粒度的情感分析任务,旨在分类出文本中不同方面的情感。目前,现有方面级情感分类模型存在特征提取层次浅、泛化能力弱等问题。为此,该文提出一种基于融合对抗网络的方面级情感分类模型ASFAN(Aspect-level Sentiment classification model based on Fusion Adversarial Networks)。首先,从数据集中提取文本的方面词、位置、上下文信息表示。其次,将方面词、位置、上下文信息通过BERT编码。最后,通过多头注意力和局部注意力机制提取文本特征,将特征进行融合学习。此外,通过对抗学习算法生成对抗样本,将对抗样本作为一种文本数据增强样本,优化决策边界。实验结果表明,在SemEval 2014的Restaurant、Laptop数据集和ACL-2014的Twitter数据集上,ASFAN的准确率分别达86.54%、79.15%、76.16%,ASFAN对比大多数基线模型性能提升显著。展开更多
文摘少样本学习是目前机器学习研究领域的热点和难点.针对现有的少样本学习模型不能有效捕捉数据特征与数据标签之间的联系,造成分类模型泛化能力弱的问题,提出一种基于元学习的原型空间图卷积网络少样本学习模型FSL-GCNPS(Few-Shot Learning of Graph Convolutional Network on Prototype Space).首先,利用卷积神经网络提取多任务数据的特征向量;其次,为了将特征向量映射到原型空间中,根据元学习的训练策略得到特征向量的类原型表达;然后,通过类原型向量和类向量之间的嵌入表示,构建图结构数据,并进行图卷积网络训练、推理.实验结果表明,相较于经典少样本学习方法,FSL-GCNPS模型拥有更好的分类准确率和分类稳定性.同时,在医学图像领域数据集上实验表明,FSL-GCNPS具有很好的跨域适应性.
基金financial support from the National Natural Science Foundation of China(Grant No.41827806)the Liaoning Provincial Science and Technology Program of China(Grant No.2022JH2/101300109).
文摘Microwave-assisted mechanical excavation has great application prospects in mines and tunnels,but there are few field experiments on microwave-assisted rock breaking.This paper takes the Sishanling iron mine as the research object and adopts the self-developed high-power microwave-induced fracturing test system for hard rock to conduct field experiments of microwave-induced fracturing of iron ore.The heating and reflection evolution characteristics of ore under different microwave parameters(antenna type,power,and working distance)were studied,and the optimal microwave parameters were obtained.Subsequently,the ore was irradiated with the optimal microwave parameters,and the cracking effect of the ore under the action of the high-power open microwave was analyzed.The results show that the reflection coefficient(standing wave ratio)can be rapidly(<5 s)and automatically adjusted below the preset threshold value(1.6)as microwave irradiation is performed.When using a right-angle horn antenna with a working distance of 5 cm,the effect of automatic reflection adjustment reaches the best among other antenna types and working distances.When the working distance is the same,the average temperature of the irradiation surface and the area of the high-temperature area under the action of the two antennas(right-angled and equal-angled horn antenna)are basically the same and decrease with the increase of working distance.The optimal microwave parameters are:a right-angle horn antenna with a working distance of 5 cm.Subsequently,in further experiments,the optimal parameters were used to irradiate for 20 s and 40 s at a microwave power of 60 kW,respectively.The surface damage extended 38 cm×30 cm and 53 cm×30 cm,respectively,and the damage extended to a depth of about 50 cm.The drilling speed was increased by 56.2%and 66.5%,respectively,compared to the case when microwaves were not used.
文摘方面级情感分类是一种细粒度的情感分析任务,旨在分类出文本中不同方面的情感。目前,现有方面级情感分类模型存在特征提取层次浅、泛化能力弱等问题。为此,该文提出一种基于融合对抗网络的方面级情感分类模型ASFAN(Aspect-level Sentiment classification model based on Fusion Adversarial Networks)。首先,从数据集中提取文本的方面词、位置、上下文信息表示。其次,将方面词、位置、上下文信息通过BERT编码。最后,通过多头注意力和局部注意力机制提取文本特征,将特征进行融合学习。此外,通过对抗学习算法生成对抗样本,将对抗样本作为一种文本数据增强样本,优化决策边界。实验结果表明,在SemEval 2014的Restaurant、Laptop数据集和ACL-2014的Twitter数据集上,ASFAN的准确率分别达86.54%、79.15%、76.16%,ASFAN对比大多数基线模型性能提升显著。
