As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decompos...As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.展开更多
In allusion to the difficulty of integrating data with different models in integrating spatial information, the characteristics of raster structure, vector structure and mixed model were analyzed, and a hierarchical v...In allusion to the difficulty of integrating data with different models in integrating spatial information, the characteristics of raster structure, vector structure and mixed model were analyzed, and a hierarchical vector-raster integrative full feature model was put forward by integrating the advantage of vector and raster model and using the object-oriented method. The data structures of the four basic features, i.e. point, line, surface and solid, were described. An application was analyzed and described, and the characteristics of this model were described. In this model, all objects in the real world are divided into and described as features with hierarchy, and all the data are organized in vector. This model can describe data based on feature, field, network and other models, and avoid the disadvantage of inability to integrate data based on different models and perform spatial analysis on them in spatial information integration.展开更多
外来入侵植物命名实体识别是进一步挖掘入侵植物信息的关键步骤。为解决外来入侵植物领域命名实体识别存在训练数据稀缺、字符级向量表征单一、专业实体识别精度不足等问题,构建了一种基于多特征融合的外来入侵植物细粒度命名实体识别模...外来入侵植物命名实体识别是进一步挖掘入侵植物信息的关键步骤。为解决外来入侵植物领域命名实体识别存在训练数据稀缺、字符级向量表征单一、专业实体识别精度不足等问题,构建了一种基于多特征融合的外来入侵植物细粒度命名实体识别模型(invasive alien plant fine-grained named entity recognition model based on multi-feature fusion,IAPMFF)。首先,采用RoBERTa(Robustly optimized BERT approach,RoBERTa)预训练模型为基础架构,通过构建领域专用词典并通过词汇特征向量融合,增强模型对低频词及专业术语的表征能力;其次,设计双通道特征提取层,利用双向长短时记忆网络(Bi-directional long-short term memory,BiLSTM)提取长序列语义特征,结合卷积残差结构(convolution residual structure,CRS)捕获更多细粒度特征;然后,设计分层特征融合机制,通过多头自注意力机制加权融合两种特征向量,构建多维度语义表征;最后,采用条件随机场(conditional random field,CRF)进行序列解码优化。基于专家知识,构建包含24类细粒度实体标签的外来入侵植物命名实体识别数据集。试验表明,IAP-MFF模型在外来入侵植物命名实体识别数据集上取得91.51%精确率、92.51%召回率和92.01%的F1值,较基线模型分别提升4.40、3.39、3.91个百分点,显著改善了小样本细粒度实体的识别效果。在Weibo、Resume公共数据集上F1值分别达到72.75%和97.15%,表明了模型的泛化性和优越性能。IAP-MFF模型通过融合包含领域知识在内的多种特征,有效提升实体识别精度与泛化能力,为外来入侵植物知识图谱构建奠定技术基础。展开更多
基金supported by the National Natural Science Foundation of China(62273354,61673387,61833016).
文摘As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.
基金Project (40473029) supported bythe National Natural Science Foundation of China project (04JJ3046) supported bytheNatural Science Foundation of Hunan Province , China
文摘In allusion to the difficulty of integrating data with different models in integrating spatial information, the characteristics of raster structure, vector structure and mixed model were analyzed, and a hierarchical vector-raster integrative full feature model was put forward by integrating the advantage of vector and raster model and using the object-oriented method. The data structures of the four basic features, i.e. point, line, surface and solid, were described. An application was analyzed and described, and the characteristics of this model were described. In this model, all objects in the real world are divided into and described as features with hierarchy, and all the data are organized in vector. This model can describe data based on feature, field, network and other models, and avoid the disadvantage of inability to integrate data based on different models and perform spatial analysis on them in spatial information integration.
文摘外来入侵植物命名实体识别是进一步挖掘入侵植物信息的关键步骤。为解决外来入侵植物领域命名实体识别存在训练数据稀缺、字符级向量表征单一、专业实体识别精度不足等问题,构建了一种基于多特征融合的外来入侵植物细粒度命名实体识别模型(invasive alien plant fine-grained named entity recognition model based on multi-feature fusion,IAPMFF)。首先,采用RoBERTa(Robustly optimized BERT approach,RoBERTa)预训练模型为基础架构,通过构建领域专用词典并通过词汇特征向量融合,增强模型对低频词及专业术语的表征能力;其次,设计双通道特征提取层,利用双向长短时记忆网络(Bi-directional long-short term memory,BiLSTM)提取长序列语义特征,结合卷积残差结构(convolution residual structure,CRS)捕获更多细粒度特征;然后,设计分层特征融合机制,通过多头自注意力机制加权融合两种特征向量,构建多维度语义表征;最后,采用条件随机场(conditional random field,CRF)进行序列解码优化。基于专家知识,构建包含24类细粒度实体标签的外来入侵植物命名实体识别数据集。试验表明,IAP-MFF模型在外来入侵植物命名实体识别数据集上取得91.51%精确率、92.51%召回率和92.01%的F1值,较基线模型分别提升4.40、3.39、3.91个百分点,显著改善了小样本细粒度实体的识别效果。在Weibo、Resume公共数据集上F1值分别达到72.75%和97.15%,表明了模型的泛化性和优越性能。IAP-MFF模型通过融合包含领域知识在内的多种特征,有效提升实体识别精度与泛化能力,为外来入侵植物知识图谱构建奠定技术基础。
