Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production.It has been difficult to meet the needs of the field wit...Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production.It has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hotrolled strip.Insufficient data and difficult parameter adjustment limit deep learning models based on multi-layer networks in practical applications;besides,the limited discrete process parameters used make it impossible to effectively depict the actual strip processing process.In order to solve these problems,this research proposed a new sampling approach for mechanical characteristics input data of hot-rolled strip based on the multi-grained cascade forest(gcForest)framework.According to the characteristics of complex process flow and abnormal sensitivity of process path and parameters to product quality in the hot-rolled strip production,a three-dimensional continuous time series process data sampling method based on time-temperature-deformation was designed.The basic information of strip steel(chemical composition and typical process parameters)is fused with the local process information collected by multi-grained scanning,so that the next link’s input has both local and global features.Furthermore,in the multi-grained scanning structure,a sub sampling scheme with a variable window was designed,so that input data with different dimensions can get output characteristics of the same dimension after passing through the multi-grained scanning structure,allowing the cascade forest structure to be trained normally.Finally,actual production data of three steel grades was used to conduct the experimental evaluation.The results revealed that the gcForest-based mechanical property prediction model outperforms the competition in terms of comprehensive performance,ease of parameter adjustment,and ability to sustain high prediction accuracy with fewer samples.展开更多
BACKGROUND:Early withdrawal of invasive mechanical ventilation(IMV) followed by noninvasive MV(NIMV) is a new strategy for changing modes of treatment in patients with acute exacerbations of chronic obstructive pulmon...BACKGROUND:Early withdrawal of invasive mechanical ventilation(IMV) followed by noninvasive MV(NIMV) is a new strategy for changing modes of treatment in patients with acute exacerbations of chronic obstructive pulmonary disease(AECOPD) with acute respiratory failure(ARF).Using pulmonary infection control window(PIC window) as the switch point for transferring from invasive to noninvasive MV,the time for early extubation can be more accurately judged,and therapy efficacy can be improved.This study aimed to prospectively investigate the clinical effectiveness of fiberoptic bronchscopy(FOB) in patients with AECOPD during sequential weaning of invasive-noninvasive MV.METHODS:Since July 2006 to January 2011,106 AECOPD patients with ARF were treated with comprehensive medication and IMV after hospitalization.Patients were randomly divided into two groups according to whether fiberoptic bronchoscope is used(group A,n=54) or not(group B,n=52) during sequential weaning from invasive to noninvasive MV.In group A,for sputum suction and bronchoalveolar lavage(BAL),a fiberoptic bronchoscope was put into the airway from the outside of an endotracheal tube,which was accompanied with uninterrupted use of a ventilator.After achieving PIC window,patients of both groups changed to NIMV mode,and weaned from ventilation.The following listed indices were used to compare between the groups after treatment:1) the occurrence time of PIC,the duration of MV,the length of ICU stay,the success rate of weaning from MV for the first time,the rate of reventilatJon and the occurrence rate of ventilator-associated pneumonia(VAP);2) the convenience and safety of FOB manipulation.The results were compared using Student's f test and the Chi-square test.RESULTS:The occurrence time of PIC was(5.01 ±1.49) d,(5.87±1.87) d in groups A and B,respectively(P<0.05);the duration of MV was(6.98±1.84) d,(8.69±2.41) d in groups A and B,respectively(P<0.01);the length of ICU stay was(9.25±1.84) d,(11.10±2.63) d in groups A and B,respectively(P<0.01);the success rate of weaning for the first time was 96.30%,76.92%in groups A and B,respectively(P<0.01);the rate of reventilation was 5.56%,19.23%in groups A and B,respectively(P<0.05);and the occurrence rate of VAP was 3.70%,23.07%in groups A and B,respectively(P<0.01).Moreover,it was easy and safe to manipulate FOB,and no side effect was observed.CONCLUSIONS:The application of FOB in patients with AECOPD during sequential weaning of invasive-noninvasive MV is effective in ICU.It can decrease the duration of MV and the length of ICU stay,increase the success rate from weaning MV for the first time,reduce the rate of reventilation and the occurrence rate of VAP.In addition,such a method is convenient and safe in patients of this kind.展开更多
现有的大多数兴趣点(point of interest,POI)推荐系统由于忽略了用户签到序列中的顺序行为模式,以及用户的个性化偏好对于POI推荐的影响,导致POI推荐系统性能较低,推荐结果不可靠,进而影响用户体验。为了解决上述问题,提出一种融合时序...现有的大多数兴趣点(point of interest,POI)推荐系统由于忽略了用户签到序列中的顺序行为模式,以及用户的个性化偏好对于POI推荐的影响,导致POI推荐系统性能较低,推荐结果不可靠,进而影响用户体验。为了解决上述问题,提出一种融合时序门控图神经网络的兴趣点推荐方法。运用时序门控图神经网络(temporal gated graph neural network,TGGNN)学习POI embedding;采用注意力机制捕获用户的长期偏好;通过注意力机制融合用户的最新偏好和实时偏好,进而捕获用户的短期偏好。通过自适应的方式结合用户的长期和短期偏好,计算候选POI的推荐得分,并根据得分为用户进行POI推荐。实验结果表明,与现有方法相比,该方法在召回率和平均倒数排名这两项指标上均有较为明显的提升,因此可以取得很好的推荐效果,具有良好的应用前景。展开更多
口语理解(spoken language understanding,SLU)是面向任务的对话系统的核心组成部分,旨在提取用户查询的语义框架.在对话系统中,口语理解组件(SLU)负责识别用户的请求,并创建总结用户需求的语义框架,SLU通常包括两个子任务:意图检测(int...口语理解(spoken language understanding,SLU)是面向任务的对话系统的核心组成部分,旨在提取用户查询的语义框架.在对话系统中,口语理解组件(SLU)负责识别用户的请求,并创建总结用户需求的语义框架,SLU通常包括两个子任务:意图检测(intent detection,ID)和槽位填充(slot filling,SF).意图检测是一个语义话语分类问题,在句子层面分析话语的语义;槽位填充是一个序列标注任务,在词级层面分析话语的语义.由于意图和槽之间的密切相关性,主流的工作采用联合模型来利用跨任务的共享知识.但是ID和SF是两个具有强相关性的不同任务,它们分别表征了话语的句级语义信息和词级信息,这意味着两个任务的信息是异构的,同时具有不同的粒度.提出一种用于联合意图检测和槽位填充的异构交互结构,采用自注意力和图注意力网络的联合形式充分地捕捉两个相关任务中异构信息的句级语义信息和词级信息之间的关系.不同于普通的同构结构,所提模型是一个包含不同类型节点和连接的异构图架构,因为异构图涉及更全面的信息和丰富的语义,同时可以更好地交互表征不同粒度节点之间的信息.此外,为了更好地适应槽标签的局部连续性,利用窗口机制来准确地表示词级嵌入表示.同时结合预训练模型(BERT),分析所提出模型应用预训练模型的效果.所提模型在两个公共数据集上的实验结果表明,所提模型在意图检测任务上准确率分别达到了97.98%和99.11%,在槽位填充任务上F1分数分别达到96.10%和96.11%,均优于目前主流的方法.展开更多
在室内空间进行准确的行人模式识别/场景感知,特别是与位置关联的识别/感知,对于行人的聚集或追踪具有重要意义.针对传统机器学习方法特征提取困难、分类精度低,非正常性行为造成较大识别误差等问题,提出一种基于注意力机制和双向长短记...在室内空间进行准确的行人模式识别/场景感知,特别是与位置关联的识别/感知,对于行人的聚集或追踪具有重要意义.针对传统机器学习方法特征提取困难、分类精度低,非正常性行为造成较大识别误差等问题,提出一种基于注意力机制和双向长短记忆(bidirectional long short-term memory,Bi-LSTM)网络的室内实时行人模式识别的模型.建立Bi-LSTM网络提取滑动窗口内行人模式时序特征,评估模型网络结构的性能与时效性,优化所提网络的Bi-LSTM层数和隐藏层节点数,并确定最优的网络结构;为了削减噪声数据对模型的影响,提高网络筛选信息特征的能力,引入注意力机制对所提取的时序特征进行权重参数优化.实验结果表明,相比传统机器学习算法,优化参数后的Bi-LSTM网络,行人模式识别准确度平均提高6.37%,进一步引入注意力机制后,识别准确度平均提高9.21%,最终准确度可达99.32%.所提模型可以有效对行人模式/场景感知进行分类,为室内精准定位追踪提供方法支持.展开更多
基金financially supported by the National Natural Science Foundation of China(No.52004029)the Fundamental Research Funds for the Central Universities,China(No.FRF-TT-20-06).
文摘Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production.It has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hotrolled strip.Insufficient data and difficult parameter adjustment limit deep learning models based on multi-layer networks in practical applications;besides,the limited discrete process parameters used make it impossible to effectively depict the actual strip processing process.In order to solve these problems,this research proposed a new sampling approach for mechanical characteristics input data of hot-rolled strip based on the multi-grained cascade forest(gcForest)framework.According to the characteristics of complex process flow and abnormal sensitivity of process path and parameters to product quality in the hot-rolled strip production,a three-dimensional continuous time series process data sampling method based on time-temperature-deformation was designed.The basic information of strip steel(chemical composition and typical process parameters)is fused with the local process information collected by multi-grained scanning,so that the next link’s input has both local and global features.Furthermore,in the multi-grained scanning structure,a sub sampling scheme with a variable window was designed,so that input data with different dimensions can get output characteristics of the same dimension after passing through the multi-grained scanning structure,allowing the cascade forest structure to be trained normally.Finally,actual production data of three steel grades was used to conduct the experimental evaluation.The results revealed that the gcForest-based mechanical property prediction model outperforms the competition in terms of comprehensive performance,ease of parameter adjustment,and ability to sustain high prediction accuracy with fewer samples.
文摘BACKGROUND:Early withdrawal of invasive mechanical ventilation(IMV) followed by noninvasive MV(NIMV) is a new strategy for changing modes of treatment in patients with acute exacerbations of chronic obstructive pulmonary disease(AECOPD) with acute respiratory failure(ARF).Using pulmonary infection control window(PIC window) as the switch point for transferring from invasive to noninvasive MV,the time for early extubation can be more accurately judged,and therapy efficacy can be improved.This study aimed to prospectively investigate the clinical effectiveness of fiberoptic bronchscopy(FOB) in patients with AECOPD during sequential weaning of invasive-noninvasive MV.METHODS:Since July 2006 to January 2011,106 AECOPD patients with ARF were treated with comprehensive medication and IMV after hospitalization.Patients were randomly divided into two groups according to whether fiberoptic bronchoscope is used(group A,n=54) or not(group B,n=52) during sequential weaning from invasive to noninvasive MV.In group A,for sputum suction and bronchoalveolar lavage(BAL),a fiberoptic bronchoscope was put into the airway from the outside of an endotracheal tube,which was accompanied with uninterrupted use of a ventilator.After achieving PIC window,patients of both groups changed to NIMV mode,and weaned from ventilation.The following listed indices were used to compare between the groups after treatment:1) the occurrence time of PIC,the duration of MV,the length of ICU stay,the success rate of weaning from MV for the first time,the rate of reventilatJon and the occurrence rate of ventilator-associated pneumonia(VAP);2) the convenience and safety of FOB manipulation.The results were compared using Student's f test and the Chi-square test.RESULTS:The occurrence time of PIC was(5.01 ±1.49) d,(5.87±1.87) d in groups A and B,respectively(P<0.05);the duration of MV was(6.98±1.84) d,(8.69±2.41) d in groups A and B,respectively(P<0.01);the length of ICU stay was(9.25±1.84) d,(11.10±2.63) d in groups A and B,respectively(P<0.01);the success rate of weaning for the first time was 96.30%,76.92%in groups A and B,respectively(P<0.01);the rate of reventilation was 5.56%,19.23%in groups A and B,respectively(P<0.05);and the occurrence rate of VAP was 3.70%,23.07%in groups A and B,respectively(P<0.01).Moreover,it was easy and safe to manipulate FOB,and no side effect was observed.CONCLUSIONS:The application of FOB in patients with AECOPD during sequential weaning of invasive-noninvasive MV is effective in ICU.It can decrease the duration of MV and the length of ICU stay,increase the success rate from weaning MV for the first time,reduce the rate of reventilation and the occurrence rate of VAP.In addition,such a method is convenient and safe in patients of this kind.
文摘口语理解(spoken language understanding,SLU)是面向任务的对话系统的核心组成部分,旨在提取用户查询的语义框架.在对话系统中,口语理解组件(SLU)负责识别用户的请求,并创建总结用户需求的语义框架,SLU通常包括两个子任务:意图检测(intent detection,ID)和槽位填充(slot filling,SF).意图检测是一个语义话语分类问题,在句子层面分析话语的语义;槽位填充是一个序列标注任务,在词级层面分析话语的语义.由于意图和槽之间的密切相关性,主流的工作采用联合模型来利用跨任务的共享知识.但是ID和SF是两个具有强相关性的不同任务,它们分别表征了话语的句级语义信息和词级信息,这意味着两个任务的信息是异构的,同时具有不同的粒度.提出一种用于联合意图检测和槽位填充的异构交互结构,采用自注意力和图注意力网络的联合形式充分地捕捉两个相关任务中异构信息的句级语义信息和词级信息之间的关系.不同于普通的同构结构,所提模型是一个包含不同类型节点和连接的异构图架构,因为异构图涉及更全面的信息和丰富的语义,同时可以更好地交互表征不同粒度节点之间的信息.此外,为了更好地适应槽标签的局部连续性,利用窗口机制来准确地表示词级嵌入表示.同时结合预训练模型(BERT),分析所提出模型应用预训练模型的效果.所提模型在两个公共数据集上的实验结果表明,所提模型在意图检测任务上准确率分别达到了97.98%和99.11%,在槽位填充任务上F1分数分别达到96.10%和96.11%,均优于目前主流的方法.
文摘在室内空间进行准确的行人模式识别/场景感知,特别是与位置关联的识别/感知,对于行人的聚集或追踪具有重要意义.针对传统机器学习方法特征提取困难、分类精度低,非正常性行为造成较大识别误差等问题,提出一种基于注意力机制和双向长短记忆(bidirectional long short-term memory,Bi-LSTM)网络的室内实时行人模式识别的模型.建立Bi-LSTM网络提取滑动窗口内行人模式时序特征,评估模型网络结构的性能与时效性,优化所提网络的Bi-LSTM层数和隐藏层节点数,并确定最优的网络结构;为了削减噪声数据对模型的影响,提高网络筛选信息特征的能力,引入注意力机制对所提取的时序特征进行权重参数优化.实验结果表明,相比传统机器学习算法,优化参数后的Bi-LSTM网络,行人模式识别准确度平均提高6.37%,进一步引入注意力机制后,识别准确度平均提高9.21%,最终准确度可达99.32%.所提模型可以有效对行人模式/场景感知进行分类,为室内精准定位追踪提供方法支持.
