Variational Autoencoder based Metamodeling for Multi-Objective Topology Optimization of Electrical Machines

使用变分自编码器进行器件图像-IV映射的逆向设计与正向预测

Device Image-IV Mapping using Variational Autoencoder for Inverse Design and Forward Prediction

本文提出基于变分自编码器的框架，通过堆叠两个VAE分别学习器件图像和IV曲线流形，实现逆向设计与正向预测，对噪声鲁棒且无需领域知识。

基于时序卷积自编码器和校准分类器的航天运载火箭发动机电气系统故障检测与诊断

Fault detection and diagnosis for the engine electrical system of a space launcher based on a temporal convolutional autoencoder and calibrated classifiers

本文提出一种基于时序卷积自编码器与校准梯度提升分类器的航天运载火箭发动机电气系统故障检测与诊断框架，可估计置信度、识别分布外数据并控制虚警，在仿真数据上验证了其有效性。

利用自编码器进行电气配电系统故障检测

Fault Detection in Electrical Distribution System using Autoencoders

本文提出利用深度自编码器和卷积自编码器进行电气配电系统故障检测，通过高准确率验证了方法的有效性。

Estimating a Brain Network Predictive of Stress and Genotype with Supervised Autoencoders

Targeted stimulation of the brain has the potential to treat mental illnesses. We propose an approach to help design the stimulation protocol by identifying electrical dynamics across many brain regions that relate to illness states. We model multi-region electrical activity as a superposition of activity from latent networks, where the weights on the latent networks relate to an outcome of interest. In order to improve on drawbacks of latent factor modeling in this context, we focus on supervised autoencoders (SAEs), which can improve predictive performance while maintaining a generative mode…

面向心电图任务特异性特征提取的$\eta$-VAE联合优化

Joint optimization of a $β$-VAE for ECG task-specific feature extraction

本研究针对心电图特征提取问题，提出联合优化信号重建与心脏功能预测的$\eta$-VAE方法，在7255名急性冠脉综合征患者数据上验证了其相比普通模型在预测性能和可解释性上的提升。

具有局部注意力的Transformer自编码器用于稀疏和不规则时间序列的框架及其在风险估计中的应用

Transformer autoencoder with local attention for sparse and irregular time series with application on risk estimation

本文提出了一种针对稀疏不规则时间序列的风险估计框架，结合Transformer自编码器与局部注意力机制，有效捕捉稀疏数据中的相关模式，提升风险估计的准确性和鲁棒性。

利用自编码器与自动微分重构一组时间序列中的缺失变量

Using Autoencoders and AutoDiff to Reconstruct Missing Variables in a Set of Time Series

针对机器学习黑箱模型输入输出特征组合固定的问题，提出基于训练后自编码器与自动微分的时间序列缺失变量重构方法，无需重训即可调整特征组合，在强非线性电气元件数据集上验证了单变量及多变量缺失场景的有效性。

Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines

Rotating machines like engines, pumps, or turbines are ubiquitous in modern day societies. Their mechanical parts such as electrical engines, rotors, or bearings are the major components and any failure in them may result in their total shutdown. Anomaly detection in such critical systems is very important to monitor the system's health. As the requirement to obtain a dataset from rotating machines where all possible faults are explicitly labeled is difficult to satisfy, we propose a method that focuses on the normal behavior of the machine instead. We propose an autoencoder model-based method…

基于表面肌电图的电机单元参数估计方法研究

Estimation of Motor Unit Parameters from Surface Electromyograms using an Informed Autoencoder

本文提出一种基于受 inform autoencoder 的非侵入式表面肌电图数据处理方法，用于同时估计多个电机单元参数，如神经支配区中心和传导速度，通过数据驱动的机器学习减少手动建模工作量。

Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary Learning

We introduce a neural-network architecture, termed the constrained recurrent sparse autoencoder (CRsAE), that solves convolutional dictionary learning problems, thus establishing a link between dictionary learning and neural networks. Specifically, we leverage the interpretation of the alternating-minimization algorithm for dictionary learning as an approximate Expectation-Maximization algorithm to develop autoencoders that enable the simultaneous training of the dictionary and regularization parameter (ReLU bias). The forward pass of the encoder approximates the sufficient statistics of the E…

Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise

In complex industrial settings, it is common practice to monitor the operation of machines in order to detect undesired states, adjust maintenance schedules, optimize system performance or collect usage statistics of individual machines. In this work, we focus on estimating the power output of a Combined Heat and Power (CHP) machine of a medium-sized company facility by analyzing the total facility power consumption. We formulate the problem as a time-series classification problem where the class label represents the CHP power output. As the facility is fully instrumented and sensor measuremen…