Accurate and reliable fault diagnosis is critical for secure operation in complex smart power systems.While graph neural networks show promise for this task,existing methods often neglect the long-tailed distribution ...Accurate and reliable fault diagnosis is critical for secure operation in complex smart power systems.While graph neural networks show promise for this task,existing methods often neglect the long-tailed distribution inherent in real-world grid fault data and fail to provide reliability estimates for their decisions.To address these dual challenges,we propose a novel multi-expert collaboration uncertainty-aware power fault recognition framework with cross-view graph learning.Its core innovations are two synergistic modules:(1)The infographics aggregation module tackles the long-tail problem by learning robust graph-level representations.It employs an information-driven optimization loss within a contrastive graph architecture,explicitly preserving global invariance and local structural information across diverse(including rare)fault states.This ensures balanced representation learning for both the head and tail classes.(2)The multi-expert reliable decision module addresses prediction uncertainty.It trains individual expert classifiers using the Dirichlet distribution to explicitly model the credibility(uncertainty)of each expert’s decision.Crucially,a complementary collaboration rule based on evidence theory dynamically integrates these experts.This rule generates active weights for expert participation,prioritizing more certain experts and fusing their evidence to produce a final decision with a quantifiable reliability estimate.Collaboratively,these modules enable reliable diagnosis under data imbalance:The Infographics Module provides discriminative representations for all fault types,especially tail classes,while the Multi-Expert Module leverages these representations to make decisions with explicit uncertainty quantification.This synergy significantly improves both the accuracy and the reliability of fault recognition,particularly for rare or ambiguous grid conditions.Ultimately,extensive experiment evaluations on the four datasets reveal that the proposed method outperforms the state-of-the-art methods in the fault diagnosis of smart grids,in terms of accuracy,precision,f score and recall.展开更多
Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ign...Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.展开更多
在分布式环境中,数据异质性表现为数据特征差异。专家模型协同存在知识孤立与任务分配不合理的问题,导致专家训练效果参差不齐,难以充分发挥各模型优势,使得整体性能受限。针对这些问题,提出了一种基于多专家协同和信息交互的社会化学...在分布式环境中,数据异质性表现为数据特征差异。专家模型协同存在知识孤立与任务分配不合理的问题,导致专家训练效果参差不齐,难以充分发挥各模型优势,使得整体性能受限。针对这些问题,提出了一种基于多专家协同和信息交互的社会化学习框架(Social Learning Based on Multi-expert Collaboration and Information Interaction,MECII)。该框架结合混合专家模型和社会化学习思想,通过多专家协同、门控网络、自适应信息交互和门控选择约束这四大模块,优化了专家间的知识共享与互补机制,有效解决了分布式学习中的数据异质性和专家协同问题。MECII通过精准的专家选择与任务分配,促进了专家之间的信息流动,使每个专家在处理特定数据时的准确率得到提升,增强了整体模型性能。实验结果表明,MECII在CIFAR-10和CIFAR-100数据集上相比传统的联邦学习基准方法有显著的性能提升,特别是在数据异质性场景下,与先进的FedL2P方法相比,MECII将分类准确率分别提高了6.69个百分点和5.13个百分点,且有效优化了每个专家的准确率。实验结果验证了MECII在促进专家协作和提升个体精度方面具有显著优势。展开更多
针对现有的专家推荐算法忽略了用户评论中蕴含的情感表达对专家专长表征的影响,从而导致推荐准确度不高的问题,提出基于双向编码器表示-多头注意力机制(bidirectional encoder representations from transformers-multi-head attention,...针对现有的专家推荐算法忽略了用户评论中蕴含的情感表达对专家专长表征的影响,从而导致推荐准确度不高的问题,提出基于双向编码器表示-多头注意力机制(bidirectional encoder representations from transformers-multi-head attention,BERT-MHA)的深度语义增强专家推荐算法。该算法基于预训练BERT模型,融合MHA机制,自动调整用户评论对专家历史回答问题的情感注意力权重,获取专家动态专长表征,并与静态专长联合以实现专家特征文本的语义增强,表征专家综合专长;通过注意力机制识别用户问题特征;采用多层感知机建模专家综合专长与用户问题间的非线性交互,预测推荐专家的匹配度。利用好大夫网站(haodf.com)的数据进行了不同参数配置及不同算法的对比实验,实验结果表明该算法在准确率(accuracy,ACC)和曲线下的面积(area under curve,AUC)指标下明显优于其他算法,能有效提高在线问答社区的专家推荐准确度。展开更多
基金supported by the Development Department Science and Technology Project(52992624000X).
文摘Accurate and reliable fault diagnosis is critical for secure operation in complex smart power systems.While graph neural networks show promise for this task,existing methods often neglect the long-tailed distribution inherent in real-world grid fault data and fail to provide reliability estimates for their decisions.To address these dual challenges,we propose a novel multi-expert collaboration uncertainty-aware power fault recognition framework with cross-view graph learning.Its core innovations are two synergistic modules:(1)The infographics aggregation module tackles the long-tail problem by learning robust graph-level representations.It employs an information-driven optimization loss within a contrastive graph architecture,explicitly preserving global invariance and local structural information across diverse(including rare)fault states.This ensures balanced representation learning for both the head and tail classes.(2)The multi-expert reliable decision module addresses prediction uncertainty.It trains individual expert classifiers using the Dirichlet distribution to explicitly model the credibility(uncertainty)of each expert’s decision.Crucially,a complementary collaboration rule based on evidence theory dynamically integrates these experts.This rule generates active weights for expert participation,prioritizing more certain experts and fusing their evidence to produce a final decision with a quantifiable reliability estimate.Collaboratively,these modules enable reliable diagnosis under data imbalance:The Infographics Module provides discriminative representations for all fault types,especially tail classes,while the Multi-Expert Module leverages these representations to make decisions with explicit uncertainty quantification.This synergy significantly improves both the accuracy and the reliability of fault recognition,particularly for rare or ambiguous grid conditions.Ultimately,extensive experiment evaluations on the four datasets reveal that the proposed method outperforms the state-of-the-art methods in the fault diagnosis of smart grids,in terms of accuracy,precision,f score and recall.
基金supported in part by the National Key Research and Development Program of China(2020YFB1313002)the National Natural Science Foundation of China(62276023,U22B2055,62222302,U2013202)+1 种基金the Fundamental Research Funds for the Central Universities(FRF-TP-22-003C1)the Postgraduate Education Reform Project of Henan Province(2021SJGLX260Y)。
文摘Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.
文摘在分布式环境中,数据异质性表现为数据特征差异。专家模型协同存在知识孤立与任务分配不合理的问题,导致专家训练效果参差不齐,难以充分发挥各模型优势,使得整体性能受限。针对这些问题,提出了一种基于多专家协同和信息交互的社会化学习框架(Social Learning Based on Multi-expert Collaboration and Information Interaction,MECII)。该框架结合混合专家模型和社会化学习思想,通过多专家协同、门控网络、自适应信息交互和门控选择约束这四大模块,优化了专家间的知识共享与互补机制,有效解决了分布式学习中的数据异质性和专家协同问题。MECII通过精准的专家选择与任务分配,促进了专家之间的信息流动,使每个专家在处理特定数据时的准确率得到提升,增强了整体模型性能。实验结果表明,MECII在CIFAR-10和CIFAR-100数据集上相比传统的联邦学习基准方法有显著的性能提升,特别是在数据异质性场景下,与先进的FedL2P方法相比,MECII将分类准确率分别提高了6.69个百分点和5.13个百分点,且有效优化了每个专家的准确率。实验结果验证了MECII在促进专家协作和提升个体精度方面具有显著优势。
文摘针对现有的专家推荐算法忽略了用户评论中蕴含的情感表达对专家专长表征的影响,从而导致推荐准确度不高的问题,提出基于双向编码器表示-多头注意力机制(bidirectional encoder representations from transformers-multi-head attention,BERT-MHA)的深度语义增强专家推荐算法。该算法基于预训练BERT模型,融合MHA机制,自动调整用户评论对专家历史回答问题的情感注意力权重,获取专家动态专长表征,并与静态专长联合以实现专家特征文本的语义增强,表征专家综合专长;通过注意力机制识别用户问题特征;采用多层感知机建模专家综合专长与用户问题间的非线性交互,预测推荐专家的匹配度。利用好大夫网站(haodf.com)的数据进行了不同参数配置及不同算法的对比实验,实验结果表明该算法在准确率(accuracy,ACC)和曲线下的面积(area under curve,AUC)指标下明显优于其他算法,能有效提高在线问答社区的专家推荐准确度。
