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

Pure Edge computing (PEC) aims to bring cloud applications and services to
the edge of the network to support the growing user demand for time-sensitive
applications and data-driven computing. However, mobility and limited
computational capacity of edge devices pose challenges in supporting some
urgent and computationally intensive tasks with strict response time demands.
If the execution results of these tasks exceed the deadline, they become
worthless and can cause severe safety issues. Therefore, it is essential to
ensure that edge nodes complete as many latency-sensitive tasks as possible.
\\In this paper, we propose a Robust Adaptive Workload Orchestration
(R-AdWOrch) model to minimize deadline misses and data loss by using priority
definition and a reallocation strategy. The results show that R-AdWOrch can
minimize deadline misses of urgent tasks while minimizing the data loss of
lower priority tasks under all conditions.

本文提出了一种鲁棒自适应工作负载编排（R-AdWOrch）模型，通过使用优先级定义和重新分配策略，最小化紧急任务的截止日期延迟和低优先级任务的数据丢失，结果表明 R-AdWOrch 在所有情况下都能最小化紧急任务的截止日期延迟，并最小化低优先级任务的数据丢失。

纯边缘计算中的鲁棒自适应工作负载编排

A Robust Adaptive Workload Orchestration in Pure Edge Computing

Wireless sensor networks are among the most promising technologies of the
current era because of their small size, lower cost, and ease of deployment.
With the increasing number of wireless sensors, the probability of generating
missing data also rises. This incomplete data could lead to disastrous
consequences if used for decision-making. There is rich literature dealing with
this problem. However, most approaches show performance degradation when a
sizable amount of data is lost. Inspired by the emerging field of graph signal
processing, this paper performs a new study of a Sobolev reconstruction
algorithm in wireless sensor networks. Experimental comparisons on several
publicly available datasets demonstrate that the algorithm surpasses multiple
state-of-the-art techniques by a maximum margin of 54%. We further show that
this algorithm consistently retrieves the missing data even during massive data
loss situations.

本文基于图信号处理理论，针对无线传感器网络中缺失数据问题，提出一种新的 Sobolev 重建算法，并在公开数据集上实验测试，表现优于现有技术