This study proposes a pose estimation-convolutional neural network-bidirectional gated recurrent unit(PSECNN-BiGRU)fusion model for human posture recognition to address low accuracy issues in abnormal posture recognit...This study proposes a pose estimation-convolutional neural network-bidirectional gated recurrent unit(PSECNN-BiGRU)fusion model for human posture recognition to address low accuracy issues in abnormal posture recognition due to the loss of some feature information and the deterioration of comprehensive performance in model detection in complex home environments.Firstly,the deep convolutional network is integrated with the Mediapipe framework to extract high-precision,multi-dimensional information from the key points of the human skeleton,thereby obtaining a human posture feature set.Thereafter,a double-layer BiGRU algorithm is utilized to extract multi-layer,bidirectional temporal features from the human posture feature set,and a CNN network with an exponential linear unit(ELU)activation function is adopted to perform deep convolution of the feature map to extract the spatial feature of the human posture.Furthermore,a squeeze and excitation networks(SENet)module is introduced to adaptively learn the importance weights of each channel,enhancing the network’s focus on important features.Finally,comparative experiments are performed on available datasets,including the public human activity recognition using smartphone dataset(UCIHAR),the public human activity recognition 70 plus dataset(HAR70PLUS),and the independently developed home abnormal behavior recognition dataset(HABRD)created by the authors’team.The results show that the average accuracy of the proposed PSE-CNN-BiGRU fusion model for human posture recognition is 99.56%,89.42%,and 98.90%,respectively,which are 5.24%,5.83%,and 3.19%higher than the average accuracy of the five models proposed in the comparative literature,including CNN,GRU,and others.The F1-score for abnormal posture recognition reaches 98.84%(heartache),97.18%(fall),99.6%(bellyache),and 98.27%(climbing)on the self-builtHABRDdataset,thus verifying the effectiveness,generalization,and robustness of the proposed model in enhancing human posture recognition.展开更多
主要介绍了激活函数的定义、作用和发展现状,并对常见激活函数的特点及其对深度神经网络性能的影响进行了简单分析,对基于TensorFlow框架使用Kaggle比赛数据集(cat vs dog)分别使用ReLu和ELU激活函数进行图像分类识别的对比实验,为目前...主要介绍了激活函数的定义、作用和发展现状,并对常见激活函数的特点及其对深度神经网络性能的影响进行了简单分析,对基于TensorFlow框架使用Kaggle比赛数据集(cat vs dog)分别使用ReLu和ELU激活函数进行图像分类识别的对比实验,为目前激活函数的研究提供相关参考.展开更多
为实现锅炉过热汽温的预测优化控制,基于某600 MW超临界机组的运行数据,采用堆栈自编码器(stacked autoencoder,SAE)建立其过热汽温特性预测模型。为加快模型训练误差梯度的下降速度,引入自适应矩估计(adaptive moment estimation,Adam...为实现锅炉过热汽温的预测优化控制,基于某600 MW超临界机组的运行数据,采用堆栈自编码器(stacked autoencoder,SAE)建立其过热汽温特性预测模型。为加快模型训练误差梯度的下降速度,引入自适应矩估计(adaptive moment estimation,Adam)算法对SAE模型进行优化;对使用不同隐含层激活函数时预测模型精度的变化情况进行比较后,选择效果更好的elu激活函数。将基于以上策略建立的Adam-SAE过热汽温预测模型与采用随机搜索优化的极端梯度提升模型的预测效果进行对比,结果表明,Adam-SAE模型的预测误差更小、精度更高。展开更多
目的:建立大鼠血浆中刺五加苷 E 和刺五加苷 B 的 RP-HPLC 分析方法,并对这2种成分在大鼠体内过程特性进行分析研究。方法:用乙腈沉淀生物样品中的蛋白质,同时又将生物样品中的刺五加苷 B 和苷 E 提取出来,然后用固相萃取法分离提取物,...目的:建立大鼠血浆中刺五加苷 E 和刺五加苷 B 的 RP-HPLC 分析方法,并对这2种成分在大鼠体内过程特性进行分析研究。方法:用乙腈沉淀生物样品中的蛋白质,同时又将生物样品中的刺五加苷 B 和苷 E 提取出来,然后用固相萃取法分离提取物,用60%甲醇将刺五加苷 B 和苷 E 从固相萃取小柱洗脱下来,用高效液相色谱法提取出来测定,色谱柱为 KromasilODS(4.6mm×250mm,5pan),柱温25℃,水-乙腈梯度流动相(0→15min,90:10→80:20;15→25min,80:20→50:50),流速0.8mL·min^(-1),一次进样,分别在220nm和206nm 波长下检测刺五加苷 E 和苷 B。结果:Wister 大鼠一次股静脉给药后血药浓度时间曲线呈三室模型,刺五加苷 E 和苷 B 的消除半衰期 t_(1/2)分别为4.66和2.49h。在主要组织中的分布特点是:刺五加苷 E 在血液、肾脏、心脏、肝脏和脾脏中都有分布,刺五加苷 B 在血液、肾脏、心脏、肝脏中都有分布,在脾脏中没有分布,刺五加苷 E 和苷 B 主要由肝、肾代谢、排泄。结论:本法可用于刺五加苷 E 和刺五加苷 B 体内过程的研究。样品直接用固相萃取小柱处理,可消除内源性成分干扰。展开更多
基金funded by the Henan Provincial Science and Technology Research Project(222102210086)the Starry Sky Creative Space Innovation Space Innovation Incubation Project of Zhengzhou University of Light Industry(2023ZCKJ211).
文摘This study proposes a pose estimation-convolutional neural network-bidirectional gated recurrent unit(PSECNN-BiGRU)fusion model for human posture recognition to address low accuracy issues in abnormal posture recognition due to the loss of some feature information and the deterioration of comprehensive performance in model detection in complex home environments.Firstly,the deep convolutional network is integrated with the Mediapipe framework to extract high-precision,multi-dimensional information from the key points of the human skeleton,thereby obtaining a human posture feature set.Thereafter,a double-layer BiGRU algorithm is utilized to extract multi-layer,bidirectional temporal features from the human posture feature set,and a CNN network with an exponential linear unit(ELU)activation function is adopted to perform deep convolution of the feature map to extract the spatial feature of the human posture.Furthermore,a squeeze and excitation networks(SENet)module is introduced to adaptively learn the importance weights of each channel,enhancing the network’s focus on important features.Finally,comparative experiments are performed on available datasets,including the public human activity recognition using smartphone dataset(UCIHAR),the public human activity recognition 70 plus dataset(HAR70PLUS),and the independently developed home abnormal behavior recognition dataset(HABRD)created by the authors’team.The results show that the average accuracy of the proposed PSE-CNN-BiGRU fusion model for human posture recognition is 99.56%,89.42%,and 98.90%,respectively,which are 5.24%,5.83%,and 3.19%higher than the average accuracy of the five models proposed in the comparative literature,including CNN,GRU,and others.The F1-score for abnormal posture recognition reaches 98.84%(heartache),97.18%(fall),99.6%(bellyache),and 98.27%(climbing)on the self-builtHABRDdataset,thus verifying the effectiveness,generalization,and robustness of the proposed model in enhancing human posture recognition.
文摘为实现锅炉过热汽温的预测优化控制,基于某600 MW超临界机组的运行数据,采用堆栈自编码器(stacked autoencoder,SAE)建立其过热汽温特性预测模型。为加快模型训练误差梯度的下降速度,引入自适应矩估计(adaptive moment estimation,Adam)算法对SAE模型进行优化;对使用不同隐含层激活函数时预测模型精度的变化情况进行比较后,选择效果更好的elu激活函数。将基于以上策略建立的Adam-SAE过热汽温预测模型与采用随机搜索优化的极端梯度提升模型的预测效果进行对比,结果表明,Adam-SAE模型的预测误差更小、精度更高。
文摘目的:建立大鼠血浆中刺五加苷 E 和刺五加苷 B 的 RP-HPLC 分析方法,并对这2种成分在大鼠体内过程特性进行分析研究。方法:用乙腈沉淀生物样品中的蛋白质,同时又将生物样品中的刺五加苷 B 和苷 E 提取出来,然后用固相萃取法分离提取物,用60%甲醇将刺五加苷 B 和苷 E 从固相萃取小柱洗脱下来,用高效液相色谱法提取出来测定,色谱柱为 KromasilODS(4.6mm×250mm,5pan),柱温25℃,水-乙腈梯度流动相(0→15min,90:10→80:20;15→25min,80:20→50:50),流速0.8mL·min^(-1),一次进样,分别在220nm和206nm 波长下检测刺五加苷 E 和苷 B。结果:Wister 大鼠一次股静脉给药后血药浓度时间曲线呈三室模型,刺五加苷 E 和苷 B 的消除半衰期 t_(1/2)分别为4.66和2.49h。在主要组织中的分布特点是:刺五加苷 E 在血液、肾脏、心脏、肝脏和脾脏中都有分布,刺五加苷 B 在血液、肾脏、心脏、肝脏中都有分布,在脾脏中没有分布,刺五加苷 E 和苷 B 主要由肝、肾代谢、排泄。结论:本法可用于刺五加苷 E 和刺五加苷 B 体内过程的研究。样品直接用固相萃取小柱处理,可消除内源性成分干扰。
