With the development of computer technology, embedded control system plays an important role in modern industry. For the embedded system, traditional development methods are time-consuming and system is not easy to ma...With the development of computer technology, embedded control system plays an important role in modern industry. For the embedded system, traditional development methods are time-consuming and system is not easy to maintain. Domain-specific modeling provides a solution for the problems. In this paper, we proposed development architecture for embedded control systems based on MIC. GME is used to construct meta-model and application model, model in-terpreter interprets model and stores model information in xml format document. The final cross-platform codes are automatically generated by different templates and xml format document. This development method can reduce time and cost in the lifecycle of system development.展开更多
国际疾病分类(ICD)编码的频率分布呈现出长尾的情况,因此,对少样本编码进行多标签文本分类极具挑战性。针对少样本编码分类中训练数据不足的问题,提出了一种基于元网络的自动ICD编码模型(MNIC)。首先,将特征空间中的实例和语义空间中的...国际疾病分类(ICD)编码的频率分布呈现出长尾的情况,因此,对少样本编码进行多标签文本分类极具挑战性。针对少样本编码分类中训练数据不足的问题,提出了一种基于元网络的自动ICD编码模型(MNIC)。首先,将特征空间中的实例和语义空间中的特征拟合到同一个空间进行映射,并将频繁编码的特征表示映射到它的分类器权重上,从而通过元网络学习到元知识;然后将学习到的元知识从数据丰富的频繁编码转移到数据贫乏的少样本编码;最后,为元知识的可转移性和通用性提供了合理的解释。在MIMIC-Ⅲ数据集上的实验结果表明,与次优的AGM-HT(Adversarial Generative Model conditioned on code descriptions with Hierarchical Tree structure)模型相比,MNIC将少样本编码的Micro-F1与曲线下面积(Micro-AUC)分别提高了3.77和3.82个百分点,显著提高了少样本编码分类的性能。展开更多
文摘With the development of computer technology, embedded control system plays an important role in modern industry. For the embedded system, traditional development methods are time-consuming and system is not easy to maintain. Domain-specific modeling provides a solution for the problems. In this paper, we proposed development architecture for embedded control systems based on MIC. GME is used to construct meta-model and application model, model in-terpreter interprets model and stores model information in xml format document. The final cross-platform codes are automatically generated by different templates and xml format document. This development method can reduce time and cost in the lifecycle of system development.
文摘国际疾病分类(ICD)编码的频率分布呈现出长尾的情况,因此,对少样本编码进行多标签文本分类极具挑战性。针对少样本编码分类中训练数据不足的问题,提出了一种基于元网络的自动ICD编码模型(MNIC)。首先,将特征空间中的实例和语义空间中的特征拟合到同一个空间进行映射,并将频繁编码的特征表示映射到它的分类器权重上,从而通过元网络学习到元知识;然后将学习到的元知识从数据丰富的频繁编码转移到数据贫乏的少样本编码;最后,为元知识的可转移性和通用性提供了合理的解释。在MIMIC-Ⅲ数据集上的实验结果表明,与次优的AGM-HT(Adversarial Generative Model conditioned on code descriptions with Hierarchical Tree structure)模型相比,MNIC将少样本编码的Micro-F1与曲线下面积(Micro-AUC)分别提高了3.77和3.82个百分点,显著提高了少样本编码分类的性能。
