A new pattern recognition method of shape was presented based on artificial neural network theory.The method avoids the defects of shape pattern recognition with polynomials and it has strong disturbance resistance.It...A new pattern recognition method of shape was presented based on artificial neural network theory.The method avoids the defects of shape pattern recognition with polynomials and it has strong disturbance resistance.It has been proved to be superior in recognizing different shape patterns by identifying many sorts of working sample books which the results are known.展开更多
Standard Penetration Test（SPT） and Cone Penetration Test（CPT） are the most frequently used field tests to estimate soil parameters for geotechnical analysis and design.Numerous soil parameters are related to the S...Standard Penetration Test（SPT） and Cone Penetration Test（CPT） are the most frequently used field tests to estimate soil parameters for geotechnical analysis and design.Numerous soil parameters are related to the SPT N-value.In contrast,CPT is becoming more popular for site investigation and geotechnical design.Correlation of CPT data with SPT N-value is very beneficial since most of the field parameters are related to SPT N-values.A back-propagation artificial neural network（ANN） model was developed to predict the N6o-value from CPT data.Data used in this study consisted of 109 CPT-SPT pairs for sand,sandy silt,and silty sand soils.The ANN model input variables are：CPT tip resistance（qc）,effective vertical stress（σ’v）,and CPT sleeve friction（fs）.A different set of SPT-CPT data was used to check the reliability of the developed ANN model.It was shown that ANN model either under-predicted the N60-value by 7-16%or over-predicted it by 7-20%.It is concluded that back-propagation neural networks is a good tool to predict N60-value from CPT data with acceptable accuracy.展开更多
The packet loss classification has always been a hot and difficult issue in TCP congestion control research.Compared with the terrestrial network,the probability of packet loss in LEO satellite network increases drama...The packet loss classification has always been a hot and difficult issue in TCP congestion control research.Compared with the terrestrial network,the probability of packet loss in LEO satellite network increases dramatically.What’s more,the problem of concept drifting is also more serious,which greatly affects the accuracy of the loss classification model.In this paper,we propose a new loss classification scheme based on concept drift detection and hybrid integration learning for LEO satellite networks,named LDM-Satellite,which consists of three modules:concept drift detection,lost packet cache and hybrid integration classification.As far,this is the first paper to consider the influence of concept drift on the loss classification model in satellite networks.We also innovatively use multiple base classifiers and a naive Bayes classifier as the final hybrid classifier.And a new weight algorithm for these classifiers is given.In ns-2 simulation,LDM-Satellite has a better AUC(0.9885)than the single-model machine learning classification algorithms.The accuracy of loss classification even exceeds 98%,higher than traditional TCP protocols.Moreover,compared with the existing protocols used for satellite networks,LDM-Satellite not only improves the throughput rate but also has good fairness.展开更多
Automatic target recognition （ATR） is an important issue for military applications, the topic of the ATR system belongs to the field of pattern recognition and classification. In the paper, we present an approach fo...Automatic target recognition （ATR） is an important issue for military applications, the topic of the ATR system belongs to the field of pattern recognition and classification. In the paper, we present an approach for building an ATR system with improved artificial neural network to recog- nize and classify the typical targets in the battle field. The invariant features of Hu invariant moments and roundness were selected to be the inputs of the neural network because they have the invari- ances of rotation, translation and scaling. The pictures of the targets are generated by the 3-D mod- els to improve the recognition rate because it is necessary to provide enough pictures for training the artificial neural network. The simulations prove that the approach can be implement ed in the ATR system and it has a high recognition rate and can be applied in real time.展开更多
To realize the straw biomass industrialized development,it should speed up building crop straw resource recycle logistics network, increasing straw recycle efficiency,and reducing straw utilization cost. On the basis ...To realize the straw biomass industrialized development,it should speed up building crop straw resource recycle logistics network, increasing straw recycle efficiency,and reducing straw utilization cost. On the basis of studying straw recycle process,this paper presents innovative concept and property of straw recycle logistics network,analyses design thinking of straw recycle logistics network,and works out straw recycle logistics mode and network topological structure. Finally,it comes up with construction and operation strategies of the straw logistics network from infrastructure,organization network,and information platform.展开更多
Graph-based image classification has emerged as a powerful alternative to traditional convolutional approaches,leveraging the relational structure between image regions to improve accuracy.This paper presents an enhan...Graph-based image classification has emerged as a powerful alternative to traditional convolutional approaches,leveraging the relational structure between image regions to improve accuracy.This paper presents an enhanced graph-based image classification framework that integrates convolutional neural network(CNN)features with graph convolutional network(GCN)learning,leveraging superpixel-based image representations.The proposed framework initiates the process by segmenting input images into significant superpixels,reducing computational complexity while preserving essential spatial structures.A pre-trained CNN backbone extracts both global and local features from these superpixels,capturing critical texture and shape information.These features are structured into a graph,and the framework presents a graph classification model that learns and propagates relationships between nodes,improving global contextual understanding.By combining the strengths of CNN-based feature extraction and graph-based relational learning,the method achieves higher accuracy,faster training speeds,and greater robustness in image classification tasks.Experimental evaluations on four agricultural datasets demonstrate the proposed model’s superior performance,achieving accuracy rates of 96.57%,99.63%,95.19%,and 90.00%on Tomato Leaf Disease,Dragon Fruit,Tomato Ripeness,and Dragon Fruit and Leaf datasets,respectively.The model consistently outperforms conventional CNN(89.27%–94.23%accuracy),VIT(89.45%–99.77%accuracy),VGG16(93.97%–99.52%accuracy),and ResNet50(86.67%–99.26%accuracy)methods across all datasets,with particularly significant improvements on challenging datasets such as Tomato Ripeness(95.19%vs.86.67%–94.44%)and Dragon Fruit and Leaf(90.00%vs.82.22%–83.97%).The compact superpixel representation and efficient feature propagation mechanism further accelerate learning compared to traditional CNN and graph-based approaches.展开更多
This study is subject to the finite element and abd uc tive network method application in the multi-cavity die. In order to select the optimal cooling system parameters to minimize the warp of a die-casting die, t he ...This study is subject to the finite element and abd uc tive network method application in the multi-cavity die. In order to select the optimal cooling system parameters to minimize the warp of a die-casting die, t he Taguchi’s method and the abductive network are used. These methods are appli ed to create an efficient model with functional nodes for the considered problem . Once the cooling system parameters are developed, this network can be used to predict the warp for the die-casting die accurately. A simulated annealing (SA) optimization algorithm with a performance index is then applied to the neur al network for searching the optimal cooling system parameters, and obtain rathe r satisfactory result as compared with the corresponding finite element veri fication.展开更多
基金Project Sponsored by Excellent Youth Teacher Foundation of Education Ministry of China and Provincial Natural Science Foundation of Hebei(598275)
文摘A new pattern recognition method of shape was presented based on artificial neural network theory.The method avoids the defects of shape pattern recognition with polynomials and it has strong disturbance resistance.It has been proved to be superior in recognizing different shape patterns by identifying many sorts of working sample books which the results are known.
文摘Standard Penetration Test（SPT） and Cone Penetration Test（CPT） are the most frequently used field tests to estimate soil parameters for geotechnical analysis and design.Numerous soil parameters are related to the SPT N-value.In contrast,CPT is becoming more popular for site investigation and geotechnical design.Correlation of CPT data with SPT N-value is very beneficial since most of the field parameters are related to SPT N-values.A back-propagation artificial neural network（ANN） model was developed to predict the N6o-value from CPT data.Data used in this study consisted of 109 CPT-SPT pairs for sand,sandy silt,and silty sand soils.The ANN model input variables are：CPT tip resistance（qc）,effective vertical stress（σ’v）,and CPT sleeve friction（fs）.A different set of SPT-CPT data was used to check the reliability of the developed ANN model.It was shown that ANN model either under-predicted the N60-value by 7-16%or over-predicted it by 7-20%.It is concluded that back-propagation neural networks is a good tool to predict N60-value from CPT data with acceptable accuracy.
基金the Wireless Network Positioning and Communication Integration Research Center in BUPT for financial support
文摘The packet loss classification has always been a hot and difficult issue in TCP congestion control research.Compared with the terrestrial network,the probability of packet loss in LEO satellite network increases dramatically.What’s more,the problem of concept drifting is also more serious,which greatly affects the accuracy of the loss classification model.In this paper,we propose a new loss classification scheme based on concept drift detection and hybrid integration learning for LEO satellite networks,named LDM-Satellite,which consists of three modules:concept drift detection,lost packet cache and hybrid integration classification.As far,this is the first paper to consider the influence of concept drift on the loss classification model in satellite networks.We also innovatively use multiple base classifiers and a naive Bayes classifier as the final hybrid classifier.And a new weight algorithm for these classifiers is given.In ns-2 simulation,LDM-Satellite has a better AUC(0.9885)than the single-model machine learning classification algorithms.The accuracy of loss classification even exceeds 98%,higher than traditional TCP protocols.Moreover,compared with the existing protocols used for satellite networks,LDM-Satellite not only improves the throughput rate but also has good fairness.
基金Supported by the Ministerial Level Advanced Research Foundation(9140A01010411BQ01)the National Twelfth Five-Year Project(40405050303)
文摘Automatic target recognition （ATR） is an important issue for military applications, the topic of the ATR system belongs to the field of pattern recognition and classification. In the paper, we present an approach for building an ATR system with improved artificial neural network to recog- nize and classify the typical targets in the battle field. The invariant features of Hu invariant moments and roundness were selected to be the inputs of the neural network because they have the invari- ances of rotation, translation and scaling. The pictures of the targets are generated by the 3-D mod- els to improve the recognition rate because it is necessary to provide enough pictures for training the artificial neural network. The simulations prove that the approach can be implement ed in the ATR system and it has a high recognition rate and can be applied in real time.
基金Supported by Qinglan Project of Jiangsu Colleges and Universities and General Program of Huaiyin Institute of Technology Scientific Research Foundation (No.:HGB0905)
文摘To realize the straw biomass industrialized development,it should speed up building crop straw resource recycle logistics network, increasing straw recycle efficiency,and reducing straw utilization cost. On the basis of studying straw recycle process,this paper presents innovative concept and property of straw recycle logistics network,analyses design thinking of straw recycle logistics network,and works out straw recycle logistics mode and network topological structure. Finally,it comes up with construction and operation strategies of the straw logistics network from infrastructure,organization network,and information platform.
文摘Graph-based image classification has emerged as a powerful alternative to traditional convolutional approaches,leveraging the relational structure between image regions to improve accuracy.This paper presents an enhanced graph-based image classification framework that integrates convolutional neural network(CNN)features with graph convolutional network(GCN)learning,leveraging superpixel-based image representations.The proposed framework initiates the process by segmenting input images into significant superpixels,reducing computational complexity while preserving essential spatial structures.A pre-trained CNN backbone extracts both global and local features from these superpixels,capturing critical texture and shape information.These features are structured into a graph,and the framework presents a graph classification model that learns and propagates relationships between nodes,improving global contextual understanding.By combining the strengths of CNN-based feature extraction and graph-based relational learning,the method achieves higher accuracy,faster training speeds,and greater robustness in image classification tasks.Experimental evaluations on four agricultural datasets demonstrate the proposed model’s superior performance,achieving accuracy rates of 96.57%,99.63%,95.19%,and 90.00%on Tomato Leaf Disease,Dragon Fruit,Tomato Ripeness,and Dragon Fruit and Leaf datasets,respectively.The model consistently outperforms conventional CNN(89.27%–94.23%accuracy),VIT(89.45%–99.77%accuracy),VGG16(93.97%–99.52%accuracy),and ResNet50(86.67%–99.26%accuracy)methods across all datasets,with particularly significant improvements on challenging datasets such as Tomato Ripeness(95.19%vs.86.67%–94.44%)and Dragon Fruit and Leaf(90.00%vs.82.22%–83.97%).The compact superpixel representation and efficient feature propagation mechanism further accelerate learning compared to traditional CNN and graph-based approaches.
文摘This study is subject to the finite element and abd uc tive network method application in the multi-cavity die. In order to select the optimal cooling system parameters to minimize the warp of a die-casting die, t he Taguchi’s method and the abductive network are used. These methods are appli ed to create an efficient model with functional nodes for the considered problem . Once the cooling system parameters are developed, this network can be used to predict the warp for the die-casting die accurately. A simulated annealing (SA) optimization algorithm with a performance index is then applied to the neur al network for searching the optimal cooling system parameters, and obtain rathe r satisfactory result as compared with the corresponding finite element veri fication.
