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
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
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
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
以10种木材纹理样本为对象,研究了木材纹理参数体系的建立方法,并进行了分类识别的仿真实验。首先,针对木材纹理特点并结合类别可分性判据,构造了适于描述木材的空间灰度共生矩阵,并在此基础上提取了木材的11个纹理特征参数。其次,借助...以10种木材纹理样本为对象,研究了木材纹理参数体系的建立方法,并进行了分类识别的仿真实验。首先,针对木材纹理特点并结合类别可分性判据,构造了适于描述木材的空间灰度共生矩阵,并在此基础上提取了木材的11个纹理特征参数。其次,借助相关性分析对参数进行了特征选择,进而建立了能直接与人的感官对应的木材纹理参数体系。最后,利用 BP 神经网络分类器对木材样本进行了分类识别研究,识别率为87.50%,验证了参数体系的有效性,表明用本文提出的纹理参数体系对木材进行分类识别是可行的。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 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.
基金黑龙江省自然科学基金项目((C2004-03C0308)哈尔滨市自然科学基金项日(2004AFX X J 0 20)
文摘以10种木材纹理样本为对象,研究了木材纹理参数体系的建立方法,并进行了分类识别的仿真实验。首先,针对木材纹理特点并结合类别可分性判据,构造了适于描述木材的空间灰度共生矩阵,并在此基础上提取了木材的11个纹理特征参数。其次,借助相关性分析对参数进行了特征选择,进而建立了能直接与人的感官对应的木材纹理参数体系。最后,利用 BP 神经网络分类器对木材样本进行了分类识别研究,识别率为87.50%,验证了参数体系的有效性,表明用本文提出的纹理参数体系对木材进行分类识别是可行的。
