A new methodology was proposed for contamination source identification using information provided by consumer complaints from a probabilistic view.Due to the high uncertainties of information derived from users,the ob...A new methodology was proposed for contamination source identification using information provided by consumer complaints from a probabilistic view.Due to the high uncertainties of information derived from users,the objective of the proposed methodology doesn't aim to capture a unique solution,but to minimize the number of possible contamination sources.In the proposed methodology,all the possible pollution nodes are identified through the CSA methodology firstly.And then based on the principle of total probability formula,the probability of each possible contamination node is obtained through a series of calculation.According to magnitude of the probability,the number of possible pollution nodes is minimized.The effectiveness and feasibility of the methodology is demonstrated through an application to a real case of ZJ City.Four scenarios were designed to investigate the influence of different uncertainties on the results in this case.The results show that pollutant concentration,injection duration,the number of consumer complaints nodes used for calculation and the prior probability with which consumers would complaint have no particular effect on the identification of contamination source.Three nodes were selected as the most possible pollution sources in water pipe network of ZJ City which includes more than 3 000 nodes.The results show the potential of the proposed method to identify contamination source through consumer complaints.展开更多
Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the ...Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.展开更多
A novel statistical method based on particle filtering is presented for multiple vehicle acoustic signals separation problem in wireless sensor network. The particle filtering method is able to deal with non-Gaussian ...A novel statistical method based on particle filtering is presented for multiple vehicle acoustic signals separation problem in wireless sensor network. The particle filtering method is able to deal with non-Gaussian and nonlinear models and non-stationary sources. Using some instantaneously mixed observations of several real-world vehicle acoustic signals, the proposed statistical method is compared with a conventional non-stationary Blind Source Separation algorithm and attractive simulation results are achieved. Moreover, considering the natural convenience to transmit particles between sensor nodes, the algorithm based on particle filtering is believed to have potential to enable the task of multiple vehicles recognition collaboratively performed by sensor nodes in distributed wireless sensor network.展开更多
The routing protocols play an important role for ad hoc networks performance.As some problems with DSR,SMR,and AMR protocols were analyzed,a new routing protocol suitable for UWB Ad hoc networks was proposed in this p...The routing protocols play an important role for ad hoc networks performance.As some problems with DSR,SMR,and AMR protocols were analyzed,a new routing protocol suitable for UWB Ad hoc networks was proposed in this paper.The new routing protocol utilize an act of orientation of UWB and tries to get sufficient route information and decrease the network load caused by route discovery at the same time.Simulation results show that the routing load of the new protocol is lower and throughput is higher than that of DSR.While the node’s mobility increases,these advantages become more obvious.展开更多
面向开源项目推荐开发人员对开源生态建设具有重要意义。区别于传统软件开发,开源领域的开发者、项目、组织及相互关系体现了开放式协作项目的特点,而它们蕴含的语义有助于精准推荐开源项目的开发者。因此,提出一种基于协作贡献网络(CCN...面向开源项目推荐开发人员对开源生态建设具有重要意义。区别于传统软件开发,开源领域的开发者、项目、组织及相互关系体现了开放式协作项目的特点,而它们蕴含的语义有助于精准推荐开源项目的开发者。因此,提出一种基于协作贡献网络(CCN)的开发者推荐(DRCCN)方法。首先,利用开源软件(OSS)开发者、OSS项目、OSS组织之间的贡献关系构建CCN;其次,基于CCN构建一个3层深度的异构GraphSAGE(Graph SAmple and aggreGatE)图神经网络(GNN)模型,预测开发者节点和开源项目节点之间的链接,从而产生相应的嵌入对;最后,根据预测结果,采用K最近邻(KNN)算法完成开发者推荐。在GitHub数据集上训练和测试模型的实验结果表明,相较于序列推荐的对比学习模型CL4SRec(Contrastive Learning for Sequential Recommendation),DRCCN在精确率、召回率和F1值这3个指标上分别提升了约10.7%、2.6%和4.2%。因此,所提模型可以为开源社区项目的开发者推荐提供重要的参考依据。展开更多
面向规模化屋顶光伏接入配电网急需进行有序控制的现状,提出了一种考虑源荷匹配特性的屋顶光伏并网的综合排序方法。首先,综合考虑负荷与光伏协调特性等需求,设计了兼顾光伏业主侧、电网侧和用电用户侧需求的综合评价指标体系;其次,提...面向规模化屋顶光伏接入配电网急需进行有序控制的现状,提出了一种考虑源荷匹配特性的屋顶光伏并网的综合排序方法。首先,综合考虑负荷与光伏协调特性等需求,设计了兼顾光伏业主侧、电网侧和用电用户侧需求的综合评价指标体系;其次,提出了一种基于改进层次分析法(improved analytic hierarchy process,IAHP)-改进反熵权法(improved anti-entropy method,IAM)-博弈组合赋权法-改进逼近理想解法(improved technique for order preference by similarity to ideal solution,improved TOPSIS)的评价方法,先根据改进的层次分析法进行主观赋权,同时考虑到指标间的相关性和波动性,采用所提改进反熵权法确定各指标的客观权重,再基于博弈论思想获取综合权重,以确保权重的合理性,然后,为提高各方案的整体区分度,采用所提改进逼近理想解法对屋顶光伏接入方案进行排序。最后,以IEEE 33节点系统为例,在MATLAB平台验证了所提指标体系和排序方法的有效性。展开更多
基金Project(50908165) supported by the National Natural Science Foundation of China
文摘A new methodology was proposed for contamination source identification using information provided by consumer complaints from a probabilistic view.Due to the high uncertainties of information derived from users,the objective of the proposed methodology doesn't aim to capture a unique solution,but to minimize the number of possible contamination sources.In the proposed methodology,all the possible pollution nodes are identified through the CSA methodology firstly.And then based on the principle of total probability formula,the probability of each possible contamination node is obtained through a series of calculation.According to magnitude of the probability,the number of possible pollution nodes is minimized.The effectiveness and feasibility of the methodology is demonstrated through an application to a real case of ZJ City.Four scenarios were designed to investigate the influence of different uncertainties on the results in this case.The results show that pollutant concentration,injection duration,the number of consumer complaints nodes used for calculation and the prior probability with which consumers would complaint have no particular effect on the identification of contamination source.Three nodes were selected as the most possible pollution sources in water pipe network of ZJ City which includes more than 3 000 nodes.The results show the potential of the proposed method to identify contamination source through consumer complaints.
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation under Grant No.2022M720419。
文摘Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.
基金the National "863" High Technology Development Program (2006AA01Z216)the MajorResearch Program of the Science and Technology Commission of Shanghai Municipality of China (054SGA1001).
文摘A novel statistical method based on particle filtering is presented for multiple vehicle acoustic signals separation problem in wireless sensor network. The particle filtering method is able to deal with non-Gaussian and nonlinear models and non-stationary sources. Using some instantaneously mixed observations of several real-world vehicle acoustic signals, the proposed statistical method is compared with a conventional non-stationary Blind Source Separation algorithm and attractive simulation results are achieved. Moreover, considering the natural convenience to transmit particles between sensor nodes, the algorithm based on particle filtering is believed to have potential to enable the task of multiple vehicles recognition collaboratively performed by sensor nodes in distributed wireless sensor network.
基金National Nature Science Foundation of China (No. 60496311)Nature Science Foundation of Jiangsu Province (No. BK2004067&BK2005409)Foundation of Huawei Technology (No. YJCB2004018NP).
文摘The routing protocols play an important role for ad hoc networks performance.As some problems with DSR,SMR,and AMR protocols were analyzed,a new routing protocol suitable for UWB Ad hoc networks was proposed in this paper.The new routing protocol utilize an act of orientation of UWB and tries to get sufficient route information and decrease the network load caused by route discovery at the same time.Simulation results show that the routing load of the new protocol is lower and throughput is higher than that of DSR.While the node’s mobility increases,these advantages become more obvious.
文摘面向开源项目推荐开发人员对开源生态建设具有重要意义。区别于传统软件开发,开源领域的开发者、项目、组织及相互关系体现了开放式协作项目的特点,而它们蕴含的语义有助于精准推荐开源项目的开发者。因此,提出一种基于协作贡献网络(CCN)的开发者推荐(DRCCN)方法。首先,利用开源软件(OSS)开发者、OSS项目、OSS组织之间的贡献关系构建CCN;其次,基于CCN构建一个3层深度的异构GraphSAGE(Graph SAmple and aggreGatE)图神经网络(GNN)模型,预测开发者节点和开源项目节点之间的链接,从而产生相应的嵌入对;最后,根据预测结果,采用K最近邻(KNN)算法完成开发者推荐。在GitHub数据集上训练和测试模型的实验结果表明,相较于序列推荐的对比学习模型CL4SRec(Contrastive Learning for Sequential Recommendation),DRCCN在精确率、召回率和F1值这3个指标上分别提升了约10.7%、2.6%和4.2%。因此,所提模型可以为开源社区项目的开发者推荐提供重要的参考依据。
文摘面向规模化屋顶光伏接入配电网急需进行有序控制的现状,提出了一种考虑源荷匹配特性的屋顶光伏并网的综合排序方法。首先,综合考虑负荷与光伏协调特性等需求,设计了兼顾光伏业主侧、电网侧和用电用户侧需求的综合评价指标体系;其次,提出了一种基于改进层次分析法(improved analytic hierarchy process,IAHP)-改进反熵权法(improved anti-entropy method,IAM)-博弈组合赋权法-改进逼近理想解法(improved technique for order preference by similarity to ideal solution,improved TOPSIS)的评价方法,先根据改进的层次分析法进行主观赋权,同时考虑到指标间的相关性和波动性,采用所提改进反熵权法确定各指标的客观权重,再基于博弈论思想获取综合权重,以确保权重的合理性,然后,为提高各方案的整体区分度,采用所提改进逼近理想解法对屋顶光伏接入方案进行排序。最后,以IEEE 33节点系统为例,在MATLAB平台验证了所提指标体系和排序方法的有效性。
