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Medcheck:A novel software for automated de-formulation of traditional Chinese medicine(TCM)prescriptions by liquid chromatography-mass spectrometry
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作者 Xiao-lan Li Jian-qing Zhang +7 位作者 Yun Li xuan-jing shen Huan-ya Yang Lin Yang Meng Xu Qi-rui Bi Chang-liang Yao De-an Guo 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2024年第6期930-932,共3页
Prescriptions are the main clinical application of traditional Chinese medicines(TCMs).Common forms include Chinese patent medicines,Kampo formulas,and hospital decoctions.A new pre-scription called“famous classical ... Prescriptions are the main clinical application of traditional Chinese medicines(TCMs).Common forms include Chinese patent medicines,Kampo formulas,and hospital decoctions.A new pre-scription called“famous classical formulas”is recently developed and expected to boom in the market.Identifying constituent me-dicinal plants in prescriptions is critical for new drug development and quality control[1],which could avoid safety issues from adulteration or substandard ingredients,as seen in the notorious Longdan Xiegan Pill event. 展开更多
关键词 MEDICINES FORMULATION PRESCRIPTION
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基于卷积网络结构重参数化的车位状态检测算法 被引量:1
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作者 申铉京 刘同壮 +1 位作者 王玉 刘嘉伟 《吉林大学学报(工学版)》 EI CAS CSCD 北大核心 2022年第12期2898-2905,共8页
为解决停车位状态检测算法速度慢、精度低的问题,提出了一种基于卷积网络结构重参数化的车位状态检测算法。该算法利用结构重参数化解耦训练网络和推理网络。在训练时,利用不同尺度的小卷积核组成多分支结构,用于获取车位图像中局部细... 为解决停车位状态检测算法速度慢、精度低的问题,提出了一种基于卷积网络结构重参数化的车位状态检测算法。该算法利用结构重参数化解耦训练网络和推理网络。在训练时,利用不同尺度的小卷积核组成多分支结构,用于获取车位图像中局部细节特征,使网络达到较高的检测精度。训练完成后,利用结构重参数化将训练时多分支结构等价转化为单分支结构用于推理,显著提升了检测速度且不损失检测精度。实验结果表明,本文算法与其他车位状态检测算法相比,在预测精度和算法推理速度上都具有明显优势。 展开更多
关键词 计算机应用 车位状态检测 卷积神经网络 结构重参数化
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Multi-focus image fusion based on fully convolutional networks 被引量:3
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作者 Rui GUO xuan-jing shen +1 位作者 Xiao-yu DONG Xiao-li ZHANG 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2020年第7期1019-1033,共15页
We propose a multi-focus image fusion method, in which a fully convolutional network for focus detection(FD-FCN) is constructed. To obtain more precise focus detection maps, we propose to add skip layers in the networ... We propose a multi-focus image fusion method, in which a fully convolutional network for focus detection(FD-FCN) is constructed. To obtain more precise focus detection maps, we propose to add skip layers in the network to make both detailed and abstract visual information available when using FD-FCN to generate maps. A new training dataset for the proposed network is constructed based on dataset CIFAR-10. The image fusion algorithm using FD-FCN contains three steps: focus maps are obtained using FD-FCN, decision map generation occurs by applying a morphological process on the focus maps, and image fusion occurs using a decision map. We carry out several sets of experiments, and both subjective and objective assessments demonstrate the superiority of the proposed fusion method to state-of-the-art algorithms. 展开更多
关键词 Multi-focus image fusion Fully convolutional networks Skip layer Performance evaluation
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