In this paper, we propose a novel neighbor-preferential growth (NPG) network model. Theoretical analysis and numerical simulations indicate the new model can reproduce not only a scale-free degree distribution and its...In this paper, we propose a novel neighbor-preferential growth (NPG) network model. Theoretical analysis and numerical simulations indicate the new model can reproduce not only a scale-free degree distribution and its power exponent is related to the edge-adding number m, but also a small-world effect which has large clustering coefficient and small average path length. Interestingly, the clustering coefficient of the model is close to that of globally coupled network, and the average path length is close to that of star coupled network. Meanwhile, the synchronizability of the NPG model is much stronger than that of BA scale-free network, even stronger than that of synchronization-optimal growth network.展开更多
In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (...In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.展开更多
The primary function of wireless sensor networks is to gather sensor data from the monitored area. Due to faults or malicious nodes, however, the sensor data collected or reported might be wrong. Hence it is important...The primary function of wireless sensor networks is to gather sensor data from the monitored area. Due to faults or malicious nodes, however, the sensor data collected or reported might be wrong. Hence it is important to detect events in the presence of wrong sensor readings and misleading reports. In this paper, we present a neighbor-based malicious node detection scheme for wireless sensor networks. Malicious nodes are modeled as faulty nodes behaving intelligently to lead to an incorrect decision or energy depletion without being easily detected. Each sensor node makes a decision on the fault status of itself and its neighboring nodes based on the sensor readings. Most erroneous readings due to transient faults are corrected by filtering, while nodes with permanent faults are removed using confidence-level evaluation, to improve malicious node detection rate and event detection accuracy. Each node maintains confidence levels of itself and its neighbors, indicating the track records in reporting past events correctly. Computer simulation shows that most of the malicious nodes reporting against their own readings are correctly detected unless they behave similar to the normal nodes. As a result, high event detection accuracy is also maintained while achieving low false alarm rate.展开更多
In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification probl...In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification problem of moving vehicles in wireless sensor networks using acoustic signals emitted from vehicles. Three algorithms including wavelet decomposition, weighted k-nearest-neighbor and Dempster-Shafer theory are combined in this paper. Finally, we use real world experimental data to validate the classification methods. The result shows that wavelet based feature extraction method can extract stable features from acoustic signals. By fusion with Dempster's rule, the classification performance is improved.展开更多
The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function netw...The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function network. Three models based on anatomical distance, the number of common neighbor, or anatomical distance and the number of common neighbor are designed. Basing on residuals creates the evaluation criteria for selecting the optimal brain function model network in each class model. The model is selected to simulate the human real brain function network by comparison with real data functional magnetic resonance imaging(f MRI)network. Finally, the result shows that the best model only is based on anatomical distance.展开更多
在复杂网络中高质量的社团划分会更好地揭示网络的结构特性与功能。基于节点相似性的算法是一类具有代表性的社团划分算法,但现有的基于节点相似性算法没有充分考虑到共邻节点之间的联系导致准确率下降。针对上述问题,首先定义共邻节点...在复杂网络中高质量的社团划分会更好地揭示网络的结构特性与功能。基于节点相似性的算法是一类具有代表性的社团划分算法,但现有的基于节点相似性算法没有充分考虑到共邻节点之间的联系导致准确率下降。针对上述问题,首先定义共邻节点贡献度概念,提出一种基于共邻节点贡献度的社团划分算法(Contribution of Common Neighbor Nodes Based Community Division Algorithm, CCNNA),将共邻节点之间的连边数参与到RA相似度指标的计算当中,提高了度量的准确性;然后运用改进的层次聚类与最优模块度值的思想实现网络的社团划分。在人工合成网络与真实网络上的实验结果表明,所提算法能够很好地挖掘社团结构,与模块度优化CNM(Clauset-Newman-Moore)算法以及最新的基于节点相似性算法相比,所提算法有更高的社团模块度和划分准确率。展开更多
文摘In this paper, we propose a novel neighbor-preferential growth (NPG) network model. Theoretical analysis and numerical simulations indicate the new model can reproduce not only a scale-free degree distribution and its power exponent is related to the edge-adding number m, but also a small-world effect which has large clustering coefficient and small average path length. Interestingly, the clustering coefficient of the model is close to that of globally coupled network, and the average path length is close to that of star coupled network. Meanwhile, the synchronizability of the NPG model is much stronger than that of BA scale-free network, even stronger than that of synchronization-optimal growth network.
文摘In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.
文摘The primary function of wireless sensor networks is to gather sensor data from the monitored area. Due to faults or malicious nodes, however, the sensor data collected or reported might be wrong. Hence it is important to detect events in the presence of wrong sensor readings and misleading reports. In this paper, we present a neighbor-based malicious node detection scheme for wireless sensor networks. Malicious nodes are modeled as faulty nodes behaving intelligently to lead to an incorrect decision or energy depletion without being easily detected. Each sensor node makes a decision on the fault status of itself and its neighboring nodes based on the sensor readings. Most erroneous readings due to transient faults are corrected by filtering, while nodes with permanent faults are removed using confidence-level evaluation, to improve malicious node detection rate and event detection accuracy. Each node maintains confidence levels of itself and its neighbors, indicating the track records in reporting past events correctly. Computer simulation shows that most of the malicious nodes reporting against their own readings are correctly detected unless they behave similar to the normal nodes. As a result, high event detection accuracy is also maintained while achieving low false alarm rate.
基金Supported in part by Science & Technology Department of Shanghai (05dz15004)
文摘In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification problem of moving vehicles in wireless sensor networks using acoustic signals emitted from vehicles. Three algorithms including wavelet decomposition, weighted k-nearest-neighbor and Dempster-Shafer theory are combined in this paper. Finally, we use real world experimental data to validate the classification methods. The result shows that wavelet based feature extraction method can extract stable features from acoustic signals. By fusion with Dempster's rule, the classification performance is improved.
基金the National Natural Science Foundation of China(Nos.6117013661373101+3 种基金61472270 and61402318)the Natural Science Foundation of Shanxi(No.2014021022-5)the Special/Youth Foundation of Taiyuan University of Technology(No.2012L014)the Youth Team Fund of Taiyuan University of Technology(Nos.2013T047 and 2013T048)
文摘The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function network. Three models based on anatomical distance, the number of common neighbor, or anatomical distance and the number of common neighbor are designed. Basing on residuals creates the evaluation criteria for selecting the optimal brain function model network in each class model. The model is selected to simulate the human real brain function network by comparison with real data functional magnetic resonance imaging(f MRI)network. Finally, the result shows that the best model only is based on anatomical distance.
文摘在复杂网络中高质量的社团划分会更好地揭示网络的结构特性与功能。基于节点相似性的算法是一类具有代表性的社团划分算法,但现有的基于节点相似性算法没有充分考虑到共邻节点之间的联系导致准确率下降。针对上述问题,首先定义共邻节点贡献度概念,提出一种基于共邻节点贡献度的社团划分算法(Contribution of Common Neighbor Nodes Based Community Division Algorithm, CCNNA),将共邻节点之间的连边数参与到RA相似度指标的计算当中,提高了度量的准确性;然后运用改进的层次聚类与最优模块度值的思想实现网络的社团划分。在人工合成网络与真实网络上的实验结果表明,所提算法能够很好地挖掘社团结构,与模块度优化CNM(Clauset-Newman-Moore)算法以及最新的基于节点相似性算法相比,所提算法有更高的社团模块度和划分准确率。
