In order to simplify the three-dimensional building group model, this paper proposes a clustering generalization method based on visual cognitive theory. The method uses road elements to roughly divide scenes, and the...In order to simplify the three-dimensional building group model, this paper proposes a clustering generalization method based on visual cognitive theory. The method uses road elements to roughly divide scenes, and then uses spatial cognitive elements such as direction, area, height and their topological constraints to classify them precisely, so as to make them conform to the urban morphological characteristics. Delaunay triangulation network and boundary tracking synthesis algorithm are used to merge and summarize the models, and the models are stored hierarchically. The proposed algorithm should be verified experimentally with a typical urban complex model. The experimental results show that the efficiency of the method used in this paper is at least 20% higher than that of previous one, and with the growth of test data, the higher efficiency is improved. The classification results conform to human cognitive habits, and the generalization levels of different models can be relatively unified by adaptive control of each threshold in the clustering generalization process.展开更多
目的通过机器学习分析“舌边白涎”舌象特性,对舌象进行局部特征识别研究,探讨卷积神经网络算法在舌象识别应用中的性能。方法使用Python进行图像预处理,搭建用于舌象识别的视觉几何组16层(visual geometry group 16,VGG16)卷积神经网...目的通过机器学习分析“舌边白涎”舌象特性,对舌象进行局部特征识别研究,探讨卷积神经网络算法在舌象识别应用中的性能。方法使用Python进行图像预处理,搭建用于舌象识别的视觉几何组16层(visual geometry group 16,VGG16)卷积神经网络模型,分析其对“舌边白涎”舌象鉴别分析的效果,并结合热力图分析“舌边白涎”典型舌象表现。结果基于PyTorch框架,进行卷积神经网络的舌象鉴别研究,VGG16及残差网络50层(residual network 50,ResNet50)模型验证准确率均较高,达到80%以上,且ResNet50模型优于VGG16模型,可为舌象识别提供一定参考。基于加权梯度类激活映射(gradient-weighted class activation mapping,Grad-CAM)技术,通过舌苔舌色差异分布的网络可视化,有助于直观进行模型评估分析。结论基于卷积神经网络模型对舌象数据库进行分析,实现“舌边白涎”舌象识别,有助于临床诊疗的客观化辅助分析,为舌诊智能化发展提供一定借鉴。展开更多
为了解决寻常型银屑病在样本分布不平衡的数据中可能会导致的深度学习模型诊断效果下降等问题,通过结合改进模糊KMeans聚类算法对高聚类复杂度数据的处理能力以及Visual Geometry Group 13(VGG13)深度卷积神经网络模型的预测能力,提出...为了解决寻常型银屑病在样本分布不平衡的数据中可能会导致的深度学习模型诊断效果下降等问题,通过结合改进模糊KMeans聚类算法对高聚类复杂度数据的处理能力以及Visual Geometry Group 13(VGG13)深度卷积神经网络模型的预测能力,提出一种基于改进模糊KMeans聚类算法的VGG13深度卷积神经网络(VGG13-KMeans)模型,并将其应用于寻常型银屑病的诊断任务中。实验结果表明,相较于VGG13以及ResNet18两种方法,本文方法更适用于对银屑病特征的识别。展开更多
The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that...The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers.The impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the consumers.The overall purpose of the study is to classify rotten fruits,which can affect the taste,texture,and appearance of other fresh fruits,thereby reducing their shelf life.The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life.Hence,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten fruits.The proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and guavas.Similarly,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf life.The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model.In comparison to other pre-trained models,the visual geometry group16(VGG16)obtained a higher accuracy score of 95%.Likewise,the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf life.By developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated produce.Thereby,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.展开更多
由于花卉种类繁多,花卉的识别需要人们掌握深厚的植物学知识和长期观察的经验总结,而利用深度学习可实现花卉种类的智能识别。首先,通过迁移学习在视觉几何群网络(Visual Geometry Group Network,VGG-16)算法的基础上进行改进,实现花卉...由于花卉种类繁多,花卉的识别需要人们掌握深厚的植物学知识和长期观察的经验总结,而利用深度学习可实现花卉种类的智能识别。首先,通过迁移学习在视觉几何群网络(Visual Geometry Group Network,VGG-16)算法的基础上进行改进,实现花卉的识别;其次,将训练好的模型进行封装,上传至云服务器;最后,在云服务器上进行识别,通过超文本传输协议(Hyper Text Transfer Protocol,HTTP)与微信小程序进行通信,实现了拍照上传即可识别花卉种类和了解花卉特性的小程序设计。展开更多
In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dime...In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dimensional batch normalization visual geometry group(3D-BN-VGG)and long short-term memory(LSTM)network is designed.In this network,3D convolutional layer is used to extract the spatial domain features and time domain features of video sequence at the same time,multiple small convolution kernels are stacked to replace large convolution kernels,thus the depth of neural network is deepened and the number of network parameters is reduced.In addition,the latest batch normalization algorithm is added to the 3-dimensional convolutional network to improve the training speed.Then the output of the full connection layer is sent to LSTM network as the feature vectors to extract the sequence information.This method,which directly uses the output of the whole base level without passing through the full connection layer,reduces the parameters of the whole fusion network to 15324485,nearly twice as much as those of 3D-BN-VGG.Finally,it reveals that the proposed network achieves 96.5%and 74.9%accuracy in the UCF-101 and HMDB-51 respectively,and the algorithm has a calculation speed of 1066 fps and an acceleration ratio of 1,which has a significant predominance in velocity.展开更多
文摘In order to simplify the three-dimensional building group model, this paper proposes a clustering generalization method based on visual cognitive theory. The method uses road elements to roughly divide scenes, and then uses spatial cognitive elements such as direction, area, height and their topological constraints to classify them precisely, so as to make them conform to the urban morphological characteristics. Delaunay triangulation network and boundary tracking synthesis algorithm are used to merge and summarize the models, and the models are stored hierarchically. The proposed algorithm should be verified experimentally with a typical urban complex model. The experimental results show that the efficiency of the method used in this paper is at least 20% higher than that of previous one, and with the growth of test data, the higher efficiency is improved. The classification results conform to human cognitive habits, and the generalization levels of different models can be relatively unified by adaptive control of each threshold in the clustering generalization process.
文摘目的通过机器学习分析“舌边白涎”舌象特性,对舌象进行局部特征识别研究,探讨卷积神经网络算法在舌象识别应用中的性能。方法使用Python进行图像预处理,搭建用于舌象识别的视觉几何组16层(visual geometry group 16,VGG16)卷积神经网络模型,分析其对“舌边白涎”舌象鉴别分析的效果,并结合热力图分析“舌边白涎”典型舌象表现。结果基于PyTorch框架,进行卷积神经网络的舌象鉴别研究,VGG16及残差网络50层(residual network 50,ResNet50)模型验证准确率均较高,达到80%以上,且ResNet50模型优于VGG16模型,可为舌象识别提供一定参考。基于加权梯度类激活映射(gradient-weighted class activation mapping,Grad-CAM)技术,通过舌苔舌色差异分布的网络可视化,有助于直观进行模型评估分析。结论基于卷积神经网络模型对舌象数据库进行分析,实现“舌边白涎”舌象识别,有助于临床诊疗的客观化辅助分析,为舌诊智能化发展提供一定借鉴。
文摘为了解决寻常型银屑病在样本分布不平衡的数据中可能会导致的深度学习模型诊断效果下降等问题,通过结合改进模糊KMeans聚类算法对高聚类复杂度数据的处理能力以及Visual Geometry Group 13(VGG13)深度卷积神经网络模型的预测能力,提出一种基于改进模糊KMeans聚类算法的VGG13深度卷积神经网络(VGG13-KMeans)模型,并将其应用于寻常型银屑病的诊断任务中。实验结果表明,相较于VGG13以及ResNet18两种方法,本文方法更适用于对银屑病特征的识别。
文摘The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers.The impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the consumers.The overall purpose of the study is to classify rotten fruits,which can affect the taste,texture,and appearance of other fresh fruits,thereby reducing their shelf life.The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life.Hence,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten fruits.The proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and guavas.Similarly,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf life.The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model.In comparison to other pre-trained models,the visual geometry group16(VGG16)obtained a higher accuracy score of 95%.Likewise,the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf life.By developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated produce.Thereby,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.
文摘由于花卉种类繁多,花卉的识别需要人们掌握深厚的植物学知识和长期观察的经验总结,而利用深度学习可实现花卉种类的智能识别。首先,通过迁移学习在视觉几何群网络(Visual Geometry Group Network,VGG-16)算法的基础上进行改进,实现花卉的识别;其次,将训练好的模型进行封装,上传至云服务器;最后,在云服务器上进行识别,通过超文本传输协议(Hyper Text Transfer Protocol,HTTP)与微信小程序进行通信,实现了拍照上传即可识别花卉种类和了解花卉特性的小程序设计。
基金the National Natural Science Foundation of China(No.61772417,61634004,61602377)Key R&D Program Projects in Shaanxi Province(No.2017GY-060)Shaanxi Natural Science Basic Research Project(No.2018JM4018).
文摘In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dimensional batch normalization visual geometry group(3D-BN-VGG)and long short-term memory(LSTM)network is designed.In this network,3D convolutional layer is used to extract the spatial domain features and time domain features of video sequence at the same time,multiple small convolution kernels are stacked to replace large convolution kernels,thus the depth of neural network is deepened and the number of network parameters is reduced.In addition,the latest batch normalization algorithm is added to the 3-dimensional convolutional network to improve the training speed.Then the output of the full connection layer is sent to LSTM network as the feature vectors to extract the sequence information.This method,which directly uses the output of the whole base level without passing through the full connection layer,reduces the parameters of the whole fusion network to 15324485,nearly twice as much as those of 3D-BN-VGG.Finally,it reveals that the proposed network achieves 96.5%and 74.9%accuracy in the UCF-101 and HMDB-51 respectively,and the algorithm has a calculation speed of 1066 fps and an acceleration ratio of 1,which has a significant predominance in velocity.
