针对网络资源需求大于可用资源引发的网络拥塞,进而导致网络服务质量(Quality of Service,QoS)下降的问题,在分析网络拥塞控制算法的基础上,利用NS2仿真软件设计并仿真了不同类型的网络拓扑.仿真结果表明Vegas算法在同构型网络环境下具...针对网络资源需求大于可用资源引发的网络拥塞,进而导致网络服务质量(Quality of Service,QoS)下降的问题,在分析网络拥塞控制算法的基础上,利用NS2仿真软件设计并仿真了不同类型的网络拓扑.仿真结果表明Vegas算法在同构型网络环境下具有高平均吞吐量和低时延、无丢包性能,Tahoe、Reno、Newreno和Sack算法性能表现一致;而在异构型网络中由于竞争机制,Vegas算法性能相对较差,但其他算法会周期性的遭遇网络拥塞以及网络稳定性较差的情况.该结果为不同环境下的网络拥塞算法选择提供了依据.展开更多
As a key technology to realize smart services of Internet of Things（IoT）, network virtualization technology can support the network diversification and ubiquity, and improve the utilization rate of network resources...As a key technology to realize smart services of Internet of Things（IoT）, network virtualization technology can support the network diversification and ubiquity, and improve the utilization rate of network resources. This paper studies the service-ori- ented network virtualization architecture for loT services. Firstly the semantic description method for loT services is proposed, then the resource representation model and resource management model in the environment of network virtualization are presented. Based on the above models, the service-oriented virtual network architecture for loT is established. Finally, a smart campus system is designed and deployed based on the service-oriented virtual network architecture. Moreover, the proposed architecture and models are verified in experiments.展开更多
To improve the level of active traffic management,a short-term traffic flow prediction model is proposed by combining phase space reconstruction(PSR)and extreme gradient boosting(XGBoost)algorithms.Firstly,the traditi...To improve the level of active traffic management,a short-term traffic flow prediction model is proposed by combining phase space reconstruction(PSR)and extreme gradient boosting(XGBoost)algorithms.Firstly,the traditional data preprocessing method is improved.The new method uses hierarchical clustering to determine the traffic flow state and fills in missing and abnormal data according to different traffic flow states.Secondly,one-dimensional data are mapped into a multidimensional data matrix through PSR,and the time series complex network is used to verify the data reconstruction effect.Finally,the multidimensional data matrix is inputted into the XGBoost model to predict future traffic flow parameters.The experimental results show that the mean square error,average absolute error,and average absolute percentage error of the prediction results of the PSR-XGBoost model are 5.399%,1.632%,and 6.278%,respectively,and the required running time is 17.35 s.Compared with mathematical-statistical models and other machine learning models,the PSR-XGBoost model has clear advantages in multiple predictive indicators,proving its feasibility and superiority in short-term traffic flow prediction.展开更多
The conventional X-ray gray weighted image fusion method based on variable energy cannot characterize the phys- ical properties of complicated objects correctly, therefore, the gray correction method of X-ray fusion i...The conventional X-ray gray weighted image fusion method based on variable energy cannot characterize the phys- ical properties of complicated objects correctly, therefore, the gray correction method of X-ray fusion image based on neural network is proposed. The conventional method acquires 12 bit images on variable energy, and then fuses the images in a tra- ditional way. While the new method takes the fusion image as the input of neural network simulation system and takes the acquired 16 bit image as the output of neural network. The X-ray image physical characteristic model based on neural net- work is obtained through training. And then it takes steel ladder block as the test object to verify the feasibility of the mod- el. In the end, the gray curve of output image is compared with the gray curve of 16 bit real image. The experiment results show that this method can fit the nonlinear relationship between the fusion image and the real image, and also can expand the scope of application of low dynamic image acquisition equipment.展开更多
In this study, a Multi-Layer BP neural network(MLBP) with dynamic thresholds is employed to build a classifier model.As to the design of the neural network structure, theoretical guidance and plentiful experiments are...In this study, a Multi-Layer BP neural network(MLBP) with dynamic thresholds is employed to build a classifier model.As to the design of the neural network structure, theoretical guidance and plentiful experiments are combined to optimize the hidden layers' parameters which include the number of hidden layers and their node numbers.The classifier with dynamic thresholds is used to standardize the output for the first time, and it improves the robustness of the model to a high level.Finally, the classifier is applied to forecast box office revenue of a movie before its theatrical release.The comparison results with the MLP method show that the MLBP classifier model achieves more satisfactory results, and it is more reliable and effective to solve the problem.展开更多
An essential characteristic of the 4th Generation(4G) wireless networks is integrating various heterogeneous wireless access networks.This paper considers the network selection for both admission and handoff strategy ...An essential characteristic of the 4th Generation(4G) wireless networks is integrating various heterogeneous wireless access networks.This paper considers the network selection for both admission and handoff strategy problems in heterogeneous network of 3G/WLAN.A novel dynamic programming algorithm is proposed by taking heterogeneous network characteristics,user mobility and different service types into account.The specificity of our approach is that it puts the situations in a new model and makes decisions in stages of different states.Simulation results validate that the proposed scheme can obtain better new call blocking and handoff dropping probability performance than traditional schemes while ensuring quality-of-services(QoS) for both real-time and data connections.展开更多
文摘针对网络资源需求大于可用资源引发的网络拥塞,进而导致网络服务质量(Quality of Service,QoS)下降的问题,在分析网络拥塞控制算法的基础上,利用NS2仿真软件设计并仿真了不同类型的网络拓扑.仿真结果表明Vegas算法在同构型网络环境下具有高平均吞吐量和低时延、无丢包性能,Tahoe、Reno、Newreno和Sack算法性能表现一致;而在异构型网络中由于竞争机制,Vegas算法性能相对较差,但其他算法会周期性的遭遇网络拥塞以及网络稳定性较差的情况.该结果为不同环境下的网络拥塞算法选择提供了依据.
基金supported by the national 973 project of China under Grants 2013CB329104the Natural Science Foundation of China under Grants 61372124,61427801,61271237,61271236Jiangsu Collaborative Innovation Center for Technology and Application of Internet of Things under Grants SJ213003
文摘As a key technology to realize smart services of Internet of Things（IoT）, network virtualization technology can support the network diversification and ubiquity, and improve the utilization rate of network resources. This paper studies the service-ori- ented network virtualization architecture for loT services. Firstly the semantic description method for loT services is proposed, then the resource representation model and resource management model in the environment of network virtualization are presented. Based on the above models, the service-oriented virtual network architecture for loT is established. Finally, a smart campus system is designed and deployed based on the service-oriented virtual network architecture. Moreover, the proposed architecture and models are verified in experiments.
基金The National Natural Science Foundation of China (No.71771019, 71871130, 71971125)the Science and Technology Special Project of Shandong Provincial Public Security Department (No. 37000000015900920210010001,37000000015900920210012001)。
文摘To improve the level of active traffic management,a short-term traffic flow prediction model is proposed by combining phase space reconstruction(PSR)and extreme gradient boosting(XGBoost)algorithms.Firstly,the traditional data preprocessing method is improved.The new method uses hierarchical clustering to determine the traffic flow state and fills in missing and abnormal data according to different traffic flow states.Secondly,one-dimensional data are mapped into a multidimensional data matrix through PSR,and the time series complex network is used to verify the data reconstruction effect.Finally,the multidimensional data matrix is inputted into the XGBoost model to predict future traffic flow parameters.The experimental results show that the mean square error,average absolute error,and average absolute percentage error of the prediction results of the PSR-XGBoost model are 5.399%,1.632%,and 6.278%,respectively,and the required running time is 17.35 s.Compared with mathematical-statistical models and other machine learning models,the PSR-XGBoost model has clear advantages in multiple predictive indicators,proving its feasibility and superiority in short-term traffic flow prediction.
基金National Natural Science Foundation of China(No.61302159,61227003,61301259)Natural Science Foundation of Shanxi Province(No.2012021011-2)+2 种基金Specialized Research Fund for the Doctoral Program of Higher Education,China(No.20121420110006)Top Science and Technology Innovation Teams of Higher Learning Institutions of Shanxi Province,ChinaProject Sponsored by Scientific Research for the Returned Overseas Chinese Scholars,Shanxi Province(No.2013-083)
文摘The conventional X-ray gray weighted image fusion method based on variable energy cannot characterize the phys- ical properties of complicated objects correctly, therefore, the gray correction method of X-ray fusion image based on neural network is proposed. The conventional method acquires 12 bit images on variable energy, and then fuses the images in a tra- ditional way. While the new method takes the fusion image as the input of neural network simulation system and takes the acquired 16 bit image as the output of neural network. The X-ray image physical characteristic model based on neural net- work is obtained through training. And then it takes steel ladder block as the test object to verify the feasibility of the mod- el. In the end, the gray curve of output image is compared with the gray curve of 16 bit real image. The experiment results show that this method can fit the nonlinear relationship between the fusion image and the real image, and also can expand the scope of application of low dynamic image acquisition equipment.
基金Supported by National Natural Science Foundation of China (No. 60573172)
文摘In this study, a Multi-Layer BP neural network(MLBP) with dynamic thresholds is employed to build a classifier model.As to the design of the neural network structure, theoretical guidance and plentiful experiments are combined to optimize the hidden layers' parameters which include the number of hidden layers and their node numbers.The classifier with dynamic thresholds is used to standardize the output for the first time, and it improves the robustness of the model to a high level.Finally, the classifier is applied to forecast box office revenue of a movie before its theatrical release.The comparison results with the MLP method show that the MLBP classifier model achieves more satisfactory results, and it is more reliable and effective to solve the problem.
基金Supported by the National Natural Science Foundation and Civil Aviation Administration of China(No.61071105)
文摘An essential characteristic of the 4th Generation(4G) wireless networks is integrating various heterogeneous wireless access networks.This paper considers the network selection for both admission and handoff strategy problems in heterogeneous network of 3G/WLAN.A novel dynamic programming algorithm is proposed by taking heterogeneous network characteristics,user mobility and different service types into account.The specificity of our approach is that it puts the situations in a new model and makes decisions in stages of different states.Simulation results validate that the proposed scheme can obtain better new call blocking and handoff dropping probability performance than traditional schemes while ensuring quality-of-services(QoS) for both real-time and data connections.
