Predicting travel trajectory of vehicles can not only provide personalized services to users,but also have a certain effect on traffic guidance and traffic control.In this paper,we build a Bayonet-Corpus based on the ...Predicting travel trajectory of vehicles can not only provide personalized services to users,but also have a certain effect on traffic guidance and traffic control.In this paper,we build a Bayonet-Corpus based on the context of traffic intersections,and use it to model a traffic network.Besides,Bidirectional Gated Recurrent Unit(Bi-GRU)is used to predict the sequence of traffic intersections in one single trajectory.Firstly,considering that real traffic networks are usually complex and disorder and cannot reflect the higher dimensional relationship among traffic intersections,this paper proposes a new traffic network modeling algorithm based on the context of traffic intersections:inspired by the probabilistic language model,a Bayonet-Corpus is constructed from traffic intersections in real trajectory sequence,so the high-dimensional similarity between corpus nodes can be used to measure the semantic relation of real traffic intersections.This algorithm maps vehicle trajectory nodes into a high-dimensional space vector,blocking complex structure of real traffic network and reconstructing the traffic network space.Then,the bayonets sequence in real traffic network is mapped into a matrix.Considering the trajectories sequence is bidirectional,and Bi-GRU can handle information from forward and backward simultaneously,we use Bi-GRU to bidirectionally model the trajectory matrix for the purpose of prediction.展开更多
Near-infrared(NIR)spectral analysis,which has the advantages of rapidness,nondestruction and high-efficiency,is widely used in the detection of feed,food and mineral.In terms of qualitative identification,it can also ...Near-infrared(NIR)spectral analysis,which has the advantages of rapidness,nondestruction and high-efficiency,is widely used in the detection of feed,food and mineral.In terms of qualitative identification,it can also be used for the discriminant analysis of medicines.Long short-term memory(LSTM)neural network,bidirectional long short-term memory(BiLSTM)neural network and gated recurrent unit(GRU)network are variants of the recurrent neural network(RNN).The potential relationship between nonlinear features learned from the sequence by these variants is used to complete the missions infields such as natural language processing,signal classification and video analysis.Since the effect of these variants in drug identification is still to be studied,this paper constructs a multiclassifier of these three variants,using compoundα-keto acid tablets produced by four manufacturers and repaglinide tablets produced by five manufacturers as the research object.Then,the paper analyzes the impacts of seven different preprocessed methods on the drug NIR data by constructing different layers of LSTM,BiLSTM and GRU networks and compares different classification model indicators and training time of each model.When the spectrum data are pre-processed by z-score normalization,the GRU-3 model has the best accuracy in all models.The BiLSTM models are better for analyzing high coincidence data.The method proposed in this paper can be further extended to other NIR spectroscopy data sets.展开更多
Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user experiences.To address this,our study presents a Personalized Adaptive...Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user experiences.To address this,our study presents a Personalized Adaptive Multi-Product Recommendation System(PAMR)leveraging transfer learning and Bi-GRU(Bidirectional Gated Recurrent Units).Using a large dataset of user reviews from Amazon and Flipkart,we employ transfer learning with pre-trained models(AlexNet,GoogleNet,ResNet-50)to extract high-level attributes from product data,ensuring effective feature representation even with limited data.Bi-GRU captures both spatial and sequential dependencies in user-item interactions.The innovation of this study lies in the innovative feature fusion technique that combines the strengths of multiple transfer learning models,and the integration of an attention mechanism within the Bi-GRU framework to prioritize relevant features.Our approach addresses the classic recommendation systems that often face challenges such as cold start along with data sparsity difficulties,by utilizing robust user and item representations.The model demonstrated an accuracy of up to 96.9%,with precision and an F1-score of 96.2%and 96.97%,respectively,on the Amazon dataset,significantly outperforming the baselines and marking a considerable advancement over traditional configurations.This study highlights the effectiveness of combining transfer learning with Bi-GRU for scalable and adaptive recommendation systems,providing a versatile solution for real-world applications.展开更多
为了准确识别气体绝缘开关柜(gas insulated switchgear,GIS)设备的异常工况,提出了一种基于加权梅尔频率谱系数单类支持向量机(Mel frequency cestrum coefficient-one class support vector machine,MFCC-OCSVM)和贝叶斯优化的门控循...为了准确识别气体绝缘开关柜(gas insulated switchgear,GIS)设备的异常工况,提出了一种基于加权梅尔频率谱系数单类支持向量机(Mel frequency cestrum coefficient-one class support vector machine,MFCC-OCSVM)和贝叶斯优化的门控循环单元(bidirectional gate recurrent unit,BiGRU)声纹识别算法。首先,利用基于F统计量的MFCC对声纹数据进行加权特征提取,突出重要特征并减弱噪声的影响,然后利用OCSVM对加权后的特征进行异常检测并去除异常值,提高数据质量。为解决样本不平衡问题,采用合成少数类过采样技术(synthetic minority over-sampling technique,SMOTE)进行声纹样本的均衡。最后,应用基于贝叶斯优化的BiGRU模型进行声纹识别。以某气体绝缘全封闭组合电器(gas insulated switchgear,GIS)为例,采集了20类不同工况下操纵机构的声音样本,与多种经典分类模型进行对比。结果显示,所提算法取得的最高平均识别准确率达到了92.8%,相比于自适应增强、朴素贝叶斯和线性判别分析算法分别提升了30.1%、14.7%和11.5%。通过消融实验进一步评估和验证了所提算法各个流程对声纹识别的实际效果和性能影响,研究成果可为GIS设备异常工况的声纹识别提供高效技术路线。展开更多
基金This research is partially supported by the National Natural Science Foundation of China(Grant No.61772098)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJZD K201900603,KJQN201900629)Chongqing Grad-uate Education Teaching Reform Project(No.yjg183081).
文摘Predicting travel trajectory of vehicles can not only provide personalized services to users,but also have a certain effect on traffic guidance and traffic control.In this paper,we build a Bayonet-Corpus based on the context of traffic intersections,and use it to model a traffic network.Besides,Bidirectional Gated Recurrent Unit(Bi-GRU)is used to predict the sequence of traffic intersections in one single trajectory.Firstly,considering that real traffic networks are usually complex and disorder and cannot reflect the higher dimensional relationship among traffic intersections,this paper proposes a new traffic network modeling algorithm based on the context of traffic intersections:inspired by the probabilistic language model,a Bayonet-Corpus is constructed from traffic intersections in real trajectory sequence,so the high-dimensional similarity between corpus nodes can be used to measure the semantic relation of real traffic intersections.This algorithm maps vehicle trajectory nodes into a high-dimensional space vector,blocking complex structure of real traffic network and reconstructing the traffic network space.Then,the bayonets sequence in real traffic network is mapped into a matrix.Considering the trajectories sequence is bidirectional,and Bi-GRU can handle information from forward and backward simultaneously,we use Bi-GRU to bidirectionally model the trajectory matrix for the purpose of prediction.
基金This research was supported by the Science and Technology Planning Project of Guangdong Province(Grant Nos.2017B020221002,2018B020207008 and 2021B1111610005)Science and Technology Planning Project of Guangzhou,Grant No.201707010410。
文摘Near-infrared(NIR)spectral analysis,which has the advantages of rapidness,nondestruction and high-efficiency,is widely used in the detection of feed,food and mineral.In terms of qualitative identification,it can also be used for the discriminant analysis of medicines.Long short-term memory(LSTM)neural network,bidirectional long short-term memory(BiLSTM)neural network and gated recurrent unit(GRU)network are variants of the recurrent neural network(RNN).The potential relationship between nonlinear features learned from the sequence by these variants is used to complete the missions infields such as natural language processing,signal classification and video analysis.Since the effect of these variants in drug identification is still to be studied,this paper constructs a multiclassifier of these three variants,using compoundα-keto acid tablets produced by four manufacturers and repaglinide tablets produced by five manufacturers as the research object.Then,the paper analyzes the impacts of seven different preprocessed methods on the drug NIR data by constructing different layers of LSTM,BiLSTM and GRU networks and compares different classification model indicators and training time of each model.When the spectrum data are pre-processed by z-score normalization,the GRU-3 model has the best accuracy in all models.The BiLSTM models are better for analyzing high coincidence data.The method proposed in this paper can be further extended to other NIR spectroscopy data sets.
文摘Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user experiences.To address this,our study presents a Personalized Adaptive Multi-Product Recommendation System(PAMR)leveraging transfer learning and Bi-GRU(Bidirectional Gated Recurrent Units).Using a large dataset of user reviews from Amazon and Flipkart,we employ transfer learning with pre-trained models(AlexNet,GoogleNet,ResNet-50)to extract high-level attributes from product data,ensuring effective feature representation even with limited data.Bi-GRU captures both spatial and sequential dependencies in user-item interactions.The innovation of this study lies in the innovative feature fusion technique that combines the strengths of multiple transfer learning models,and the integration of an attention mechanism within the Bi-GRU framework to prioritize relevant features.Our approach addresses the classic recommendation systems that often face challenges such as cold start along with data sparsity difficulties,by utilizing robust user and item representations.The model demonstrated an accuracy of up to 96.9%,with precision and an F1-score of 96.2%and 96.97%,respectively,on the Amazon dataset,significantly outperforming the baselines and marking a considerable advancement over traditional configurations.This study highlights the effectiveness of combining transfer learning with Bi-GRU for scalable and adaptive recommendation systems,providing a versatile solution for real-world applications.
