A time-series similarity measurement method based on wavelet and matrix transform was proposed,and its anti-noise ability,sensitivity and accuracy were discussed. The time-series sequences were compressed into wavelet...A time-series similarity measurement method based on wavelet and matrix transform was proposed,and its anti-noise ability,sensitivity and accuracy were discussed. The time-series sequences were compressed into wavelet subspace,and sample feature vector and orthogonal basics of sample time-series sequences were obtained by K-L transform. Then the inner product transform was carried out to project analyzed time-series sequence into orthogonal basics to gain analyzed feature vectors. The similarity was calculated between sample feature vector and analyzed feature vector by the Euclid distance. Taking fault wave of power electronic devices for example,the experimental results show that the proposed method has low dimension of feature vector,the anti-noise ability of proposed method is 30 times as large as that of plain wavelet method,the sensitivity of proposed method is 1/3 as large as that of plain wavelet method,and the accuracy of proposed method is higher than that of the wavelet singular value decomposition method. The proposed method can be applied in similarity matching and indexing for lager time series databases.展开更多
目的解决中医古籍中的中药计量规则复杂、方剂查找不便和缺乏针对古籍的现代智能应用的问题。方法总结历代计量转换标准和特殊计器计量转换标准,提出一种基于“主证+方剂名称+中药组成”特征融合的医案相似度计算方法,采用基于深度学习...目的解决中医古籍中的中药计量规则复杂、方剂查找不便和缺乏针对古籍的现代智能应用的问题。方法总结历代计量转换标准和特殊计器计量转换标准,提出一种基于“主证+方剂名称+中药组成”特征融合的医案相似度计算方法,采用基于深度学习的知识增强语义表示模型(Enhanced Representation from Knowledge IntEgration,ERNIE)模型进行模型训练;利用MySQL数据库和Django框架,设计开发一个基于深度学习推荐引擎的智能化古代方剂推荐和计量换算系统。结果采用该方法得到的总体推荐准确率达到了89.55%、精确率为94.51%、召回率为83.97%,取得了较好的效果,系统能够实现方剂智能检索和中药计量单位转换,根据用户输入的症状推荐主治功效匹配度较高的系列古方信息,并将古方按不同朝代的换算规则显示方剂组成及符合现代标准计量的中药用量。结论利用该方法进行方剂智能推荐和中药计量转换,达到了较为满意的效果,为古代方剂的智能推荐提供了一种新的思路,对中药用量的标准化具有一定的借鉴意义和临床用药参考意义。展开更多
协议转换通常用于解决不同协议之间的数据交互问题,它的本质是寻找不同协议字段之间的映射关系。传统的协议转换方法存在以下缺点:转换大多是在特定协议的基础上设计的,因而这些转换是静态的,灵活性较差,不适用于多协议转换的场景;一旦...协议转换通常用于解决不同协议之间的数据交互问题,它的本质是寻找不同协议字段之间的映射关系。传统的协议转换方法存在以下缺点:转换大多是在特定协议的基础上设计的,因而这些转换是静态的,灵活性较差,不适用于多协议转换的场景;一旦协议发生改变,就需要再次分析协议的结构和字段语义以重新构建字段之间的映射关系,从而产生指数级的工作量,降低了协议转换的效率。因此,提出基于语义相似度的通用协议转换方法,旨在通过智能的方法发掘字段间的映射关系,进而提高协议转换的效率。首先,通过BERT(Bidirectional Encoder Representations from Transformers)模型分类协议字段,并排除“不应该”存在映射关系的字段;其次,通过计算字段之间的语义相似度,推理字段之间的映射关系,进而构建字段映射表;最后,提出基于语义相似度的通用协议转换框架,并定义相关协议以进行验证。仿真实验结果表明:所提方法的字段分类精准率达到了94.44%;映射关系识别精准率达到了90.70%,相较于基于知识抽取的方法提高了13.93%。以上结果验证了所提方法的有可行性,该方法可以快速识别不同协议字段之间的映射关系,适用于无人协同中多协议转换的场景。展开更多
基金Projects(60634020, 60904077, 60874069) supported by the National Natural Science Foundation of ChinaProject(JC200903180555A) supported by the Foundation Project of Shenzhen City Science and Technology Plan of China
文摘A time-series similarity measurement method based on wavelet and matrix transform was proposed,and its anti-noise ability,sensitivity and accuracy were discussed. The time-series sequences were compressed into wavelet subspace,and sample feature vector and orthogonal basics of sample time-series sequences were obtained by K-L transform. Then the inner product transform was carried out to project analyzed time-series sequence into orthogonal basics to gain analyzed feature vectors. The similarity was calculated between sample feature vector and analyzed feature vector by the Euclid distance. Taking fault wave of power electronic devices for example,the experimental results show that the proposed method has low dimension of feature vector,the anti-noise ability of proposed method is 30 times as large as that of plain wavelet method,the sensitivity of proposed method is 1/3 as large as that of plain wavelet method,and the accuracy of proposed method is higher than that of the wavelet singular value decomposition method. The proposed method can be applied in similarity matching and indexing for lager time series databases.
文摘目的解决中医古籍中的中药计量规则复杂、方剂查找不便和缺乏针对古籍的现代智能应用的问题。方法总结历代计量转换标准和特殊计器计量转换标准,提出一种基于“主证+方剂名称+中药组成”特征融合的医案相似度计算方法,采用基于深度学习的知识增强语义表示模型(Enhanced Representation from Knowledge IntEgration,ERNIE)模型进行模型训练;利用MySQL数据库和Django框架,设计开发一个基于深度学习推荐引擎的智能化古代方剂推荐和计量换算系统。结果采用该方法得到的总体推荐准确率达到了89.55%、精确率为94.51%、召回率为83.97%,取得了较好的效果,系统能够实现方剂智能检索和中药计量单位转换,根据用户输入的症状推荐主治功效匹配度较高的系列古方信息,并将古方按不同朝代的换算规则显示方剂组成及符合现代标准计量的中药用量。结论利用该方法进行方剂智能推荐和中药计量转换,达到了较为满意的效果,为古代方剂的智能推荐提供了一种新的思路,对中药用量的标准化具有一定的借鉴意义和临床用药参考意义。
文摘协议转换通常用于解决不同协议之间的数据交互问题,它的本质是寻找不同协议字段之间的映射关系。传统的协议转换方法存在以下缺点:转换大多是在特定协议的基础上设计的,因而这些转换是静态的,灵活性较差,不适用于多协议转换的场景;一旦协议发生改变,就需要再次分析协议的结构和字段语义以重新构建字段之间的映射关系,从而产生指数级的工作量,降低了协议转换的效率。因此,提出基于语义相似度的通用协议转换方法,旨在通过智能的方法发掘字段间的映射关系,进而提高协议转换的效率。首先,通过BERT(Bidirectional Encoder Representations from Transformers)模型分类协议字段,并排除“不应该”存在映射关系的字段;其次,通过计算字段之间的语义相似度,推理字段之间的映射关系,进而构建字段映射表;最后,提出基于语义相似度的通用协议转换框架,并定义相关协议以进行验证。仿真实验结果表明:所提方法的字段分类精准率达到了94.44%;映射关系识别精准率达到了90.70%,相较于基于知识抽取的方法提高了13.93%。以上结果验证了所提方法的有可行性,该方法可以快速识别不同协议字段之间的映射关系,适用于无人协同中多协议转换的场景。
