This paper discusses an accurate distributed algorithm for diffusive source localization while maintaining the low energy consumption of sensor nodes in wireless sensor networks. In this algorithm, the sensor selectio...This paper discusses an accurate distributed algorithm for diffusive source localization while maintaining the low energy consumption of sensor nodes in wireless sensor networks. In this algorithm, the sensor selection scheme based on the information utility measure is used. To update the estimation in each selected node, a neighborhood radius equal to the communication range of the sensor nodes is defined and all sensors located in the neighborhood circle, whose radius is equal to the neighborhood radius and the selected node is its centre, collaborate their information. To decrease the energy consumption, the neighborhood radius is reduced gradually based on the error covariance value of the estimation. In addition, this paper includes a new method for the initial point calculation which is important in the recursive methods used for distributed algorithms in wireless sensor networks. Numerical examples are used to study the performance of the algorithms. Simulation results show the accuracy of the new algorithm becomes better while its energy consumption is low enough.展开更多
Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or select...Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.展开更多
Information embodied in machine component classification codes has internal relation with the probability distribu- tion of the code symbol. This paper presents a model considering codes as information source based on...Information embodied in machine component classification codes has internal relation with the probability distribu- tion of the code symbol. This paper presents a model considering codes as information source based on Shannon’s information theory. Using information entropy, it preserves the mathematical form and quantitatively measures the information amount of a symbol and a bit in the machine component classification coding system. It also gets the maximum value of information amount and the corresponding coding scheme when the category of symbols is fixed. Samples are given to show how to evaluate the information amount of component codes and how to optimize a coding system.展开更多
A vast amount of information has been produced in recent years,which brings a huge challenge to information management.The better usage of big data is of important theoretical and practical significance for effectivel...A vast amount of information has been produced in recent years,which brings a huge challenge to information management.The better usage of big data is of important theoretical and practical significance for effectively addressing and managing messages.In this paper,we propose a nine-rectangle-grid information model according to the information value and privacy,and then present information use policies based on the rough set theory.Recurrent neural networks were employed to classify OTT messages.The content of user interest is effectively incorporated into the classification process during the annotation of OTT messages,ending with a reliable trained classification model.Experimental results showed that the proposed method yielded an accurate classification performance and hence can be used for effective distribution and control of OTT messages.展开更多
Background:During the early stages of the COVID-19 pandemic in China,social interactions shifted to online spaces due to lockdowns and social distancing measures.As a result,the impact of online social networking on u...Background:During the early stages of the COVID-19 pandemic in China,social interactions shifted to online spaces due to lockdowns and social distancing measures.As a result,the impact of online social networking on users’emotional status has become stronger than ever.This study examines the association between online social networking and Internet users’emotional status and how offline reality affects this relationship.Methods:The study utilizes cross-sectional online survey data(n=3004)and Baidu Migration big data from the first 3 months of the pandemic.Two dimensions of online networking are measured:social support and information sources.Results:First,individuals’online social support(β=0.16,p<0.05)and information sources(β=0.08,p<0.01)are both positively associated to their emotional status during the epidemic.Second,these positive associations are moderated by social status and provincial pandemic control interventions.With regards to the moderation effect of social status,the constructive impact of information sources on emotional well-being is more pronounced among individuals from vulnerable groups compared to those who are not.With regard to the moderation effect of provincial interventions,online social support has the potential to alleviate the adverse repercussions of high rates of confirmed COVID-19 cases and strict lockdown measures while simultaneously augmenting the favorable effects of recovery.Conclusion:The various dimensions of social networking exert distinct effects on emotional status through diverse mechanisms,all of which must be taken into account when designing and adapting pandemic-control interventions.展开更多
针对由于血管类间具有强相似性造成的动静脉错误分类问题,提出了一种新的融合上下文信息的多尺度视网膜动静脉分类网络(multi-scale retinal artery and vein classification network,MCFNet),该网络使用多尺度特征(multi-scale feature...针对由于血管类间具有强相似性造成的动静脉错误分类问题,提出了一种新的融合上下文信息的多尺度视网膜动静脉分类网络(multi-scale retinal artery and vein classification network,MCFNet),该网络使用多尺度特征(multi-scale feature,MSF)提取模块及高效的全局上下文信息融合(efficient global contextual information aggregation,EGCA)模块结合U型分割网络进行动静脉分类,抑制了倾向于背景的特征并增强了血管的边缘、交点和末端特征,解决了段内动静脉错误分类问题。此外,在U型网络的解码器部分加入3层深度监督,使浅层信息得到充分训练,避免梯度消失,优化训练过程。在2个公开的眼底图像数据集(DRIVE-AV,LES-AV)上,与3种现有网络进行方法对比,该模型的F1评分分别提高了2.86、1.92、0.81个百分点,灵敏度分别提高了4.27、2.43、1.21个百分点,结果表明所提出的模型能够很好地解决动静脉分类错误的问题。展开更多
智能电表故障的准确预测对实现计量设备精准主动运维、保障电网稳定运行具有重要意义。电表各故障类型样本的出现频次不同,且不同故障类型样本在高维特征空间中的分布存在重叠,这极大增加了故障预测的难度。现有不平衡分类方法通过构建...智能电表故障的准确预测对实现计量设备精准主动运维、保障电网稳定运行具有重要意义。电表各故障类型样本的出现频次不同,且不同故障类型样本在高维特征空间中的分布存在重叠,这极大增加了故障预测的难度。现有不平衡分类方法通过构建单一样本信息与其对应类别标签的映射关系来划分样本类型,导致对具有相似表征信息的重叠区样本难以准确判别,降低了整体分类精度。该文提出一种基于多粒度近邻图的智能电表故障分类方法。首先,选择原始数据集中样本作为目标样本,以目标样本及其近邻样本作为节点、目标样本与其近邻样本连线作为边构建近邻图。根据选择的近邻样本数量不同构建多粒度近邻图,实现目标样本的信息扩充和训练样本的数量扩增,更有利于模型稳定训练。构建编码器挖掘近邻图节点特征,利用图注意力机制,根据近邻图节点编码特征和节点邻接关系将近邻样本信息自适应地聚合到目标样本,实现对相似样本差异的有效挖掘。对于给定测试样本,通过集成测试样本多粒度近邻图的分类结果,得到更精准、更鲁棒的智能电表故障预测结果。在20个KEEL(knowledge extraction based on evolutionary learning)和UCI(UC Irvine machine learning repository)不平衡分类公开数据集和智能电表实际故障数据集上的大量实验结果表明,与17种典型方法相比,该文所提算法在处理智能电表故障分类问题上具有显著优势。展开更多
We consider a quadratic Gaussian distributed lossy source coding setup with an additional constraint of identical reconstructions between the encoder and the decoder.The setup consists of two correlated Gaussian sourc...We consider a quadratic Gaussian distributed lossy source coding setup with an additional constraint of identical reconstructions between the encoder and the decoder.The setup consists of two correlated Gaussian sources,wherein one of them has to be reconstructed to be within some distortion constraint and match with a corresponding reconstruction at the encoder,while the other source acts as coded side information.We study the tradeoff between the rates of two encoders for a given distortion constraint on the reconstruction.An explicit characterization of this trade-off is the main result of the paper.We also give close inner and outer bounds for the discrete memoryless version of the problem.展开更多
In the Saharian domain, the Tarfaya-Laayoune coastal basin developed in a stable passive margin, where asymmetrical sedimentation increase from East to West and reach a sediment stack of about 14 kilometers. However, ...In the Saharian domain, the Tarfaya-Laayoune coastal basin developed in a stable passive margin, where asymmetrical sedimentation increase from East to West and reach a sediment stack of about 14 kilometers. However, the morphology of the studied area corresponds to a vast plateau (hamada) presenting occasional major reliefs. For this purpose, remote sensing approach has been applied to find the best approaches for truthful lithological mapping. The two supervised classification methods by machine learning (Artificial Neural Network and Spectral Information Divergence) have been evaluated for a most accurate classification to be used for our lithofacies mapping. The latest geological maps and RGB images were used for pseudo-color groups to identify important areas and collect the ROIs that will serve as facilities samples for the classifications. The results obtained showed a clear distinction between the various formation units, and very close results to the field reality in the ANN classification of the studied area. Thus, the ANN method is more accurate with an overall accuracy of 92.56% and a Kappa coefficient is 0.9143.展开更多
文摘This paper discusses an accurate distributed algorithm for diffusive source localization while maintaining the low energy consumption of sensor nodes in wireless sensor networks. In this algorithm, the sensor selection scheme based on the information utility measure is used. To update the estimation in each selected node, a neighborhood radius equal to the communication range of the sensor nodes is defined and all sensors located in the neighborhood circle, whose radius is equal to the neighborhood radius and the selected node is its centre, collaborate their information. To decrease the energy consumption, the neighborhood radius is reduced gradually based on the error covariance value of the estimation. In addition, this paper includes a new method for the initial point calculation which is important in the recursive methods used for distributed algorithms in wireless sensor networks. Numerical examples are used to study the performance of the algorithms. Simulation results show the accuracy of the new algorithm becomes better while its energy consumption is low enough.
基金supported by the National Natural Science Foundation of China (67441830108 and 41871224)。
文摘Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.
基金Projects supported by the Hi-Tech Research and Development Pro-gram (863) of China (No. 2004AA84ts03) and the Science and Technology Committee of Zhejiang Province (No. 2004C31018), China
文摘Information embodied in machine component classification codes has internal relation with the probability distribu- tion of the code symbol. This paper presents a model considering codes as information source based on Shannon’s information theory. Using information entropy, it preserves the mathematical form and quantitatively measures the information amount of a symbol and a bit in the machine component classification coding system. It also gets the maximum value of information amount and the corresponding coding scheme when the category of symbols is fixed. Samples are given to show how to evaluate the information amount of component codes and how to optimize a coding system.
基金This work is supported by the Research on Big Data in Application for Education of BUPT(No.2018Y0403)Fundamental Research Funds of BUPT(No.2018XKJC07,2018RC27)the National Natural Science Foundation of China(No.61571059).
文摘A vast amount of information has been produced in recent years,which brings a huge challenge to information management.The better usage of big data is of important theoretical and practical significance for effectively addressing and managing messages.In this paper,we propose a nine-rectangle-grid information model according to the information value and privacy,and then present information use policies based on the rough set theory.Recurrent neural networks were employed to classify OTT messages.The content of user interest is effectively incorporated into the classification process during the annotation of OTT messages,ending with a reliable trained classification model.Experimental results showed that the proposed method yielded an accurate classification performance and hence can be used for effective distribution and control of OTT messages.
基金This research was funded by“the Fundamental Research Funds for the Central Universities,Grant Number XJSJ23180”,https://www.xidian.edu.cn/index.htmand“Shaanxi Province Philosophy and Social Science Research Project,Grant Number 2023QN0046”,http://www.sxsskw.org.cn/.
文摘Background:During the early stages of the COVID-19 pandemic in China,social interactions shifted to online spaces due to lockdowns and social distancing measures.As a result,the impact of online social networking on users’emotional status has become stronger than ever.This study examines the association between online social networking and Internet users’emotional status and how offline reality affects this relationship.Methods:The study utilizes cross-sectional online survey data(n=3004)and Baidu Migration big data from the first 3 months of the pandemic.Two dimensions of online networking are measured:social support and information sources.Results:First,individuals’online social support(β=0.16,p<0.05)and information sources(β=0.08,p<0.01)are both positively associated to their emotional status during the epidemic.Second,these positive associations are moderated by social status and provincial pandemic control interventions.With regards to the moderation effect of social status,the constructive impact of information sources on emotional well-being is more pronounced among individuals from vulnerable groups compared to those who are not.With regard to the moderation effect of provincial interventions,online social support has the potential to alleviate the adverse repercussions of high rates of confirmed COVID-19 cases and strict lockdown measures while simultaneously augmenting the favorable effects of recovery.Conclusion:The various dimensions of social networking exert distinct effects on emotional status through diverse mechanisms,all of which must be taken into account when designing and adapting pandemic-control interventions.
文摘针对由于血管类间具有强相似性造成的动静脉错误分类问题,提出了一种新的融合上下文信息的多尺度视网膜动静脉分类网络(multi-scale retinal artery and vein classification network,MCFNet),该网络使用多尺度特征(multi-scale feature,MSF)提取模块及高效的全局上下文信息融合(efficient global contextual information aggregation,EGCA)模块结合U型分割网络进行动静脉分类,抑制了倾向于背景的特征并增强了血管的边缘、交点和末端特征,解决了段内动静脉错误分类问题。此外,在U型网络的解码器部分加入3层深度监督,使浅层信息得到充分训练,避免梯度消失,优化训练过程。在2个公开的眼底图像数据集(DRIVE-AV,LES-AV)上,与3种现有网络进行方法对比,该模型的F1评分分别提高了2.86、1.92、0.81个百分点,灵敏度分别提高了4.27、2.43、1.21个百分点,结果表明所提出的模型能够很好地解决动静脉分类错误的问题。
文摘智能电表故障的准确预测对实现计量设备精准主动运维、保障电网稳定运行具有重要意义。电表各故障类型样本的出现频次不同,且不同故障类型样本在高维特征空间中的分布存在重叠,这极大增加了故障预测的难度。现有不平衡分类方法通过构建单一样本信息与其对应类别标签的映射关系来划分样本类型,导致对具有相似表征信息的重叠区样本难以准确判别,降低了整体分类精度。该文提出一种基于多粒度近邻图的智能电表故障分类方法。首先,选择原始数据集中样本作为目标样本,以目标样本及其近邻样本作为节点、目标样本与其近邻样本连线作为边构建近邻图。根据选择的近邻样本数量不同构建多粒度近邻图,实现目标样本的信息扩充和训练样本的数量扩增,更有利于模型稳定训练。构建编码器挖掘近邻图节点特征,利用图注意力机制,根据近邻图节点编码特征和节点邻接关系将近邻样本信息自适应地聚合到目标样本,实现对相似样本差异的有效挖掘。对于给定测试样本,通过集成测试样本多粒度近邻图的分类结果,得到更精准、更鲁棒的智能电表故障预测结果。在20个KEEL(knowledge extraction based on evolutionary learning)和UCI(UC Irvine machine learning repository)不平衡分类公开数据集和智能电表实际故障数据集上的大量实验结果表明,与17种典型方法相比,该文所提算法在处理智能电表故障分类问题上具有显著优势。
文摘We consider a quadratic Gaussian distributed lossy source coding setup with an additional constraint of identical reconstructions between the encoder and the decoder.The setup consists of two correlated Gaussian sources,wherein one of them has to be reconstructed to be within some distortion constraint and match with a corresponding reconstruction at the encoder,while the other source acts as coded side information.We study the tradeoff between the rates of two encoders for a given distortion constraint on the reconstruction.An explicit characterization of this trade-off is the main result of the paper.We also give close inner and outer bounds for the discrete memoryless version of the problem.
文摘In the Saharian domain, the Tarfaya-Laayoune coastal basin developed in a stable passive margin, where asymmetrical sedimentation increase from East to West and reach a sediment stack of about 14 kilometers. However, the morphology of the studied area corresponds to a vast plateau (hamada) presenting occasional major reliefs. For this purpose, remote sensing approach has been applied to find the best approaches for truthful lithological mapping. The two supervised classification methods by machine learning (Artificial Neural Network and Spectral Information Divergence) have been evaluated for a most accurate classification to be used for our lithofacies mapping. The latest geological maps and RGB images were used for pseudo-color groups to identify important areas and collect the ROIs that will serve as facilities samples for the classifications. The results obtained showed a clear distinction between the various formation units, and very close results to the field reality in the ANN classification of the studied area. Thus, the ANN method is more accurate with an overall accuracy of 92.56% and a Kappa coefficient is 0.9143.
