With the rapid development of intelligent video surveillance technology,pedestrian re-identification has become increasingly important inmulti-camera surveillance systems.This technology plays a critical role in enhan...With the rapid development of intelligent video surveillance technology,pedestrian re-identification has become increasingly important inmulti-camera surveillance systems.This technology plays a critical role in enhancing public safety.However,traditional methods typically process images and text separately,applying upstream models directly to downstream tasks.This approach significantly increases the complexity ofmodel training and computational costs.Furthermore,the common class imbalance in existing training datasets limitsmodel performance improvement.To address these challenges,we propose an innovative framework named Person Re-ID Network Based on Visual Prompt Technology andMulti-Instance Negative Pooling(VPM-Net).First,we incorporate the Contrastive Language-Image Pre-training(CLIP)pre-trained model to accurately map visual and textual features into a unified embedding space,effectively mitigating inconsistencies in data distribution and the training process.To enhancemodel adaptability and generalization,we introduce an efficient and task-specific Visual Prompt Tuning(VPT)technique,which improves the model’s relevance to specific tasks.Additionally,we design two key modules:the Knowledge-Aware Network(KAN)and theMulti-Instance Negative Pooling(MINP)module.The KAN module significantly enhances the model’s understanding of complex scenarios through deep contextual semantic modeling.MINP module handles samples,effectively improving the model’s ability to distinguish fine-grained features.The experimental outcomes across diverse datasets underscore the remarkable performance of VPM-Net.These results vividly demonstrate the unique advantages and robust reliability of VPM-Net in fine-grained retrieval tasks.展开更多
This study focuses on tool condition recognition through data-driven approaches to enhance the intelligence level of computerized numerical control(CNC)machining processes and improve tool utilization efficiency.Tradi...This study focuses on tool condition recognition through data-driven approaches to enhance the intelligence level of computerized numerical control(CNC)machining processes and improve tool utilization efficiency.Traditional tool monitoring methods that rely on empirical knowledge or limited mathematical models struggle to adapt to complex and dynamic machining environments.To address this,we implement real-time tool condition recognition by introducing deep learning technology.Aiming to the insufficient recognition accuracy,we propose a pyramid pooling-based vision Transformer network(P2ViT-Net)method for tool condition recognition.Using images as input effectively mitigates the issue of low-dimensional signal features.We enhance the vision Transformer(ViT)framework for image classification by developing the P2ViT model and adapt it to tool condition recognition.Experimental results demonstrate that our improved P2ViT model achieves 94.4%recognition accuracy,showing a 10%improvement over conventional ViT and outperforming all comparative convolutional neural network models.展开更多
The continuous increase of human mobility combined with a relevant use of private vehicles contributes to increase the ill effects of vehicle externalities on the environment, e.g. high levels of air pollution, toxic ...The continuous increase of human mobility combined with a relevant use of private vehicles contributes to increase the ill effects of vehicle externalities on the environment, e.g. high levels of air pollution, toxic emissions, noise pollution, and on the quality of life, e.g. parking problem, traffic congestion, and increase in the number of crashes and accidents. Transport demand management plays a very critical role in achieving greenhouse gas emission reduction targets. This study demonstrates that car pooling (CP) is an effective strategy to reduce transport volumes, transportation costs and related hill externalities in agreement with EU programs of emissions reduction targets. This paper presents an original approach to solve the CP problem. It is based on hierarchical clustering models, which have been adopted by an original decision support system (DSS). The DSS helps mobility managers to generate the pools and to design feasible paths for shared vehicles. A significant case studies and obtained results by the application of the proposed models are illustrated. They demonstrate the effectiveness of the approach and the supporting decisions tool.展开更多
In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of ...In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of multivariate time series on different scales are pooled and aggregated by spatial pyramid pooling to construct n levels feature pooling matrices on the same scale. Secondly,Deng's multivariate grey incidence model is introduced to measure the degree of incidence between feature pooling matrices at each level. Thirdly, grey incidence degrees at each level are integrated into a global incidence degree. Finally, the performance of the proposed model is verified on two data sets compared with a variety of algorithms. The results illustrate that the proposed model is more effective and efficient than other similarity measure algorithms.展开更多
The cultivar Ganoderma lucidum Hunong 5 was obtained using cross-breeding. Hunong 5 has high commercial value due to its high polysaccharide and triterpene content, This is the first report of using a DNA pooling meth...The cultivar Ganoderma lucidum Hunong 5 was obtained using cross-breeding. Hunong 5 has high commercial value due to its high polysaccharide and triterpene content, This is the first report of using a DNA pooling method to develop a stable sequence characterized amplified region （SCAR） marker for rapid identification of the G. lucidum Hunong 5 cultivar. The SCAR marker was developed by first generating and sequencing a distinctive inter simple sequence repeat （ISSR） fragment （882 bp） from G. lucidum Hunong 5 cultivar. A stable SCAR primer pair GLH5F/GLH5R were obtained to identify the cultivar and the SCAR marker is a DNA fragment of 773 bp.展开更多
In convolutional neural networks,pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models.These methods reduce the computational amount of convoluti...In convolutional neural networks,pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models.These methods reduce the computational amount of convolutional neural networks,making the neural network more efficient.Maximum pooling,average pooling,and minimum pooling methods are generally used in convolutional neural networks.However,these pooling methods are not suitable for all datasets used in neural network applications.In this study,a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural networks.This method,which we call MAM(Maximum Average Minimum)pooling,is more interactive than other traditional maximum pooling,average pooling,and minimum pooling methods and reduces data loss by calculating the more appropriate pixel value.The proposed MAM pooling method increases the performance of the neural network by calculating the optimal value during the training of convolutional neural networks.To determine the success accuracy of the proposed MAM pooling method and compare it with other traditional pooling methods,training was carried out on the LeNet-5 model using CIFAR-10,CIFAR-100,and MNIST datasets.According to the results obtained,the proposed MAM pooling method performed better than the maximum pooling,average pooling,and minimum pooling methods in all pool sizes on three different datasets.展开更多
Pooling design is a mathematical tool in many application areas. In this paper, we give a new construction of pooling design with subspaces of the pseudo-symplectic space and discuss its properties. We define the desi...Pooling design is a mathematical tool in many application areas. In this paper, we give a new construction of pooling design with subspaces of the pseudo-symplectic space and discuss its properties. We define the design parameters of a d^2-disjunct matrix. Then we discuss the change law of the design parameters in our construction along with their variables.展开更多
基金funded by the Key Research and Development Program of Hubei Province,China(Grant No.2023BEB024)the Young and Middle-aged Scientific and Technological Innova-tion Team Plan in Higher Education Institutions inHubei Province,China(GrantNo.T2023007)the key projects ofHubei Provincial Department of Education(No.D20161403).
文摘With the rapid development of intelligent video surveillance technology,pedestrian re-identification has become increasingly important inmulti-camera surveillance systems.This technology plays a critical role in enhancing public safety.However,traditional methods typically process images and text separately,applying upstream models directly to downstream tasks.This approach significantly increases the complexity ofmodel training and computational costs.Furthermore,the common class imbalance in existing training datasets limitsmodel performance improvement.To address these challenges,we propose an innovative framework named Person Re-ID Network Based on Visual Prompt Technology andMulti-Instance Negative Pooling(VPM-Net).First,we incorporate the Contrastive Language-Image Pre-training(CLIP)pre-trained model to accurately map visual and textual features into a unified embedding space,effectively mitigating inconsistencies in data distribution and the training process.To enhancemodel adaptability and generalization,we introduce an efficient and task-specific Visual Prompt Tuning(VPT)technique,which improves the model’s relevance to specific tasks.Additionally,we design two key modules:the Knowledge-Aware Network(KAN)and theMulti-Instance Negative Pooling(MINP)module.The KAN module significantly enhances the model’s understanding of complex scenarios through deep contextual semantic modeling.MINP module handles samples,effectively improving the model’s ability to distinguish fine-grained features.The experimental outcomes across diverse datasets underscore the remarkable performance of VPM-Net.These results vividly demonstrate the unique advantages and robust reliability of VPM-Net in fine-grained retrieval tasks.
基金supported by China Postdoctoral Science Foundation(No.2024M754122)the Postdoctoral Fellowship Programof CPSF(No.GZB20240972)+3 种基金the Jiangsu Funding Program for Excellent Postdoctoral Talent(No.2024ZB194)Natural Science Foundation of Jiangsu Province(No.BK20241389)Basic Science ResearchFund of China(No.JCKY2023203C026)2024 Jiangsu Province Talent Programme Qinglan Project.
文摘This study focuses on tool condition recognition through data-driven approaches to enhance the intelligence level of computerized numerical control(CNC)machining processes and improve tool utilization efficiency.Traditional tool monitoring methods that rely on empirical knowledge or limited mathematical models struggle to adapt to complex and dynamic machining environments.To address this,we implement real-time tool condition recognition by introducing deep learning technology.Aiming to the insufficient recognition accuracy,we propose a pyramid pooling-based vision Transformer network(P2ViT-Net)method for tool condition recognition.Using images as input effectively mitigates the issue of low-dimensional signal features.We enhance the vision Transformer(ViT)framework for image classification by developing the P2ViT model and adapt it to tool condition recognition.Experimental results demonstrate that our improved P2ViT model achieves 94.4%recognition accuracy,showing a 10%improvement over conventional ViT and outperforming all comparative convolutional neural network models.
文摘The continuous increase of human mobility combined with a relevant use of private vehicles contributes to increase the ill effects of vehicle externalities on the environment, e.g. high levels of air pollution, toxic emissions, noise pollution, and on the quality of life, e.g. parking problem, traffic congestion, and increase in the number of crashes and accidents. Transport demand management plays a very critical role in achieving greenhouse gas emission reduction targets. This study demonstrates that car pooling (CP) is an effective strategy to reduce transport volumes, transportation costs and related hill externalities in agreement with EU programs of emissions reduction targets. This paper presents an original approach to solve the CP problem. It is based on hierarchical clustering models, which have been adopted by an original decision support system (DSS). The DSS helps mobility managers to generate the pools and to design feasible paths for shared vehicles. A significant case studies and obtained results by the application of the proposed models are illustrated. They demonstrate the effectiveness of the approach and the supporting decisions tool.
基金supported by the National Natural Science Foundation of China(71401052)the Fundamental Research Funds for the Central Universities(2019B19514)。
文摘In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of multivariate time series on different scales are pooled and aggregated by spatial pyramid pooling to construct n levels feature pooling matrices on the same scale. Secondly,Deng's multivariate grey incidence model is introduced to measure the degree of incidence between feature pooling matrices at each level. Thirdly, grey incidence degrees at each level are integrated into a global incidence degree. Finally, the performance of the proposed model is verified on two data sets compared with a variety of algorithms. The results illustrate that the proposed model is more effective and efficient than other similarity measure algorithms.
基金financially supported by the National Natural Science Foundation of China (31401933)the Shanghai Municipal Committee of Agriculture,China (G2014070107)
文摘The cultivar Ganoderma lucidum Hunong 5 was obtained using cross-breeding. Hunong 5 has high commercial value due to its high polysaccharide and triterpene content, This is the first report of using a DNA pooling method to develop a stable sequence characterized amplified region （SCAR） marker for rapid identification of the G. lucidum Hunong 5 cultivar. The SCAR marker was developed by first generating and sequencing a distinctive inter simple sequence repeat （ISSR） fragment （882 bp） from G. lucidum Hunong 5 cultivar. A stable SCAR primer pair GLH5F/GLH5R were obtained to identify the cultivar and the SCAR marker is a DNA fragment of 773 bp.
文摘In convolutional neural networks,pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models.These methods reduce the computational amount of convolutional neural networks,making the neural network more efficient.Maximum pooling,average pooling,and minimum pooling methods are generally used in convolutional neural networks.However,these pooling methods are not suitable for all datasets used in neural network applications.In this study,a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural networks.This method,which we call MAM(Maximum Average Minimum)pooling,is more interactive than other traditional maximum pooling,average pooling,and minimum pooling methods and reduces data loss by calculating the more appropriate pixel value.The proposed MAM pooling method increases the performance of the neural network by calculating the optimal value during the training of convolutional neural networks.To determine the success accuracy of the proposed MAM pooling method and compare it with other traditional pooling methods,training was carried out on the LeNet-5 model using CIFAR-10,CIFAR-100,and MNIST datasets.According to the results obtained,the proposed MAM pooling method performed better than the maximum pooling,average pooling,and minimum pooling methods in all pool sizes on three different datasets.
基金Supported by the NSF of Hebei Province(A2009000253)
文摘Pooling design is a mathematical tool in many application areas. In this paper, we give a new construction of pooling design with subspaces of the pseudo-symplectic space and discuss its properties. We define the design parameters of a d^2-disjunct matrix. Then we discuss the change law of the design parameters in our construction along with their variables.
