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基于随机Transformer的多维时间序列异常检测模型 被引量:5
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作者 霍纬纲 梁锐 李永华 《通信学报》 EI CSCD 北大核心 2023年第2期94-103,共10页
针对已有基于变分自编码器(VAE)的多维时间序列(MTS)异常检测模型无法在隐空间中传播随机变量间的长时依赖性问题,提出了一种融合Transformer编码器和VAE的随机Transformer MTS异常检测模型(ST-MTS-AD)。在ST-MTS-AD的推断网络中,Transf... 针对已有基于变分自编码器(VAE)的多维时间序列(MTS)异常检测模型无法在隐空间中传播随机变量间的长时依赖性问题,提出了一种融合Transformer编码器和VAE的随机Transformer MTS异常检测模型(ST-MTS-AD)。在ST-MTS-AD的推断网络中,Transformer编码器产生的当前时刻MTS长时依赖特征和上一时刻随机变量的采样值被输入多层感知器,由此生成当前时刻随机变量的近似后验分布,实现随机变量间的时序依赖。采用门控转换函数(GTF)生成随机变量的先验分布,ST-MTS-AD的生成网络由多层感知器重构MTS各时刻取值分布,该多层感知器的输入为推断网络生成的MTS的长时依赖特征和随机变量近似后验采样值。ST-MTS-AD基于变分推断技术学习正常MTS样本集分布,由重构概率对数似然确定MTS异常片段。4个公开数据集上的实验表明,ST-MTS-AD模型比典型相关基线模型的F1分数有明显提升。 展开更多
关键词 随机transformer 变分自编码器 多维时间序列 异常检测
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Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics 被引量:1
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作者 Loc Vu-Quoc Alexander Humer 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第11期1069-1343,共275页
Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularl... Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example. 展开更多
关键词 Deep learning breakthroughs network architectures backpropagation stochastic optimization methods from classic to modern recurrent neural networks long short-term memory gated recurrent unit attention transformer kernel machines Gaussian processes libraries Physics-Informed Neural Networks state-of-the-art history limitations challenges Applications to computational mechanics Finite-element matrix integration improved Gauss quadrature Multiscale geomechanics fluid-filled porous media Fluid mechanics turbulence proper orthogonal decomposition Nonlinear-manifold model-order reduction autoencoder hyper-reduction using gappy data control of large deformable beam
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大范围地电位波动的监测与定位 被引量:4
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作者 黄华 邹俭 +3 位作者 阮羚 赵丹丹 苏丹 程林 《电力科学与技术学报》 CAS 北大核心 2019年第4期54-62,共9页
直流输电工程、地磁风暴和轨道交通等多个因素可能引起大范围地电位的显著波动,严重影响电力系统和管道系统的安全运行,破坏用电设备和埋地金属,产生一系列负面影响,造成重大经济损失。地电位波动能够在电力系统内产生特征电流分布,该... 直流输电工程、地磁风暴和轨道交通等多个因素可能引起大范围地电位的显著波动,严重影响电力系统和管道系统的安全运行,破坏用电设备和埋地金属,产生一系列负面影响,造成重大经济损失。地电位波动能够在电力系统内产生特征电流分布,该文首先建立典型的点形态地电位波动源的数学模型,分析单点定位原理和计算方法,并利用进化差分算法反演地电位波动源的位置和强度。基于随机仿真的思想,该文还提出利用尽量少的变压器中性点电流监测单元来更经济地构建地电位波动监测网,从而经济高效地寻找地电位波动源,预报和抑制地电位波动的不利影响。该文方法和结论也为其他形式地电位波动源的监测和定位提供关键思路。 展开更多
关键词 大地电位波动 波动源 定位方法 随机仿真 变压器中性点电流监测
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