With the widespread adoption of Non-Intrusive Load Monitoring (NILM) in
building energy management, ensuring the high quality of NILM data has become
imperative. However, practical applications of NILM face challenges associated
with data loss, significantly impacting accuracy and reliability in energy
management. This paper addresses the issue of NILM data loss by introducing an
innovative tensor completion(TC) model- Proportional-Integral-Derivative
(PID)-incorporated Non-negative Latent Factorization of Tensors (PNLFT) with
twofold ideas: 1) To tackle the issue of slow convergence in Latent
Factorization of Tensors (LFT) using Stochastic Gradient Descent (SGD), a
Proportional-Integral-Derivative controller is introduced during the learning
process. The PID controller utilizes historical and current information to
control learning residuals. 2) Considering the characteristics of NILM data,
non-negative update rules are proposed in the model's learning scheme.
Experimental results on three datasets demonstrate that, compared to
state-of-the-art models, the proposed model exhibits noteworthy enhancements in
both convergence speed and accuracy.

本文通过引入一种创新的张量完成（TC）模型 - 比例积分导数控制（PID）结合非负张量潜变分析（PNLFT）的方法来解决 NILM 数据损失的问题，实验结果表明与现有模型相比，该模型在收敛速度和准确性方面具有显著提升。

基于张量分解的缺失数据填补的非侵入式负载监测

Non-Intrusive Load Monitoring with Missing Data Imputation Based on  Tensor Decomposition

Extreme event are sudden large-amplitude changes in the state or observables
of chaotic nonlinear systems, which characterize many scientific phenomena.
Because of their violent nature, extreme events typically have adverse
consequences, which call for methods to prevent the events from happening. In
this work, we introduce the control-aware echo state network (Ca-ESN) to
seamlessly combine ESNs and control strategies, such as
proportional-integral-derivative and model predictive control, to suppress
extreme events. The methodology is showcased on a chaotic-turbulent flow, in
which we reduce the occurrence of extreme events with respect to traditional
methods by two orders of magnitude. This works opens up new possibilities for
the efficient control of nonlinear systems with neural networks.

介绍了控制感知回声状态网络（Ca-ESN），用于无缝结合 ESNs 和控制策略，如比例积分微分控制和模型预测控制，以抑制极端事件，并在混沌湍流流动中展示了这一方法，相对于传统方法将极端事件的发生率降低了两个数量级，为用神经网络有效控制非线性系统开辟了新的可能性。