DATA RECOVERY Web Results

Waste heat recoveries in data centers: A review - ScienceDirect Waste heat recovery technology is considered as a promising approach to improve energy efficiency, achieve energy and energy cost savings, and mitigate environmental impacts (caused by both carbon emission and waste heat discharge) at the same time.

Tensor network decomposition for data recovery: Recent advancements and ... Tensor network (TN) decomposition stands as a pivotal technique for characterizing the essential features of high-dimensional data, attracting significant interest and achieving notable success in high-dimensional data recovery.

Missing measurement data recovery methods in structural health ... A data recovery method based on the OMP algorithm is used in the missing response recovery for aviation anti-rust aluminum plates [32]. Li et al. [6] studied an approach that uses convex optimization theory and OMP algorithm to achieve CS-based electromechanical admittance data recovery.

False data injection attacks data recovery in smart grids: A graph ... False data injection (FDI) attacks, one of the most classical cyber attacks, have increasingly posed a significant threat to the security and reliability of power systems [5]. Such attacks, mislead the system state estimation results, by manipulating the measurements of sensors, and thereby affecting the secure operations of the power system [6]. Research on FDI attacks and their data recovery ...

Analysis of false lock in Mueller-Muller clock and data recovery system ... Another view suggests that data correlation is the key contributor [ [9], [10]]. In this work, we provide a comprehensive analysis of MMPD false lock and introduce an enhanced mitigation strategy, validated via simulations. Section 2 investigates the false-lock mechanism and presents an improved phase detection strategy.

A neural tensor decomposition model for high-order sparse data recovery ... When faced with high missing ratios or sparse observed sets, the recovery results become less ideal [20]. More importantly, the nonlinear information in the data may obscure the low rankness and the model performance may be hindered by the multi-linear hypothesis in the decomposition, making it fail to capture the nonlinear features [21].

Data centers waste heat recovery technologies: Review and evaluation With a growing focus on energy-saving and emission-reduction efforts in data centers, waste heat recovery technology is urgently needed because of the…

Kernel Bayesian tensor ring decomposition for multiway data recovery With the aid of side information, the recovery performance of the methods above is improved. However, all matrix-based methods with side information encounter difficulties when dealing with high-order data because they cannot incorporate specific side information enhancements.

Data center waste heat for district heating networks: A review This paper investigates the integration of data center waste heat into district heating networks, evaluating its potential from technical, energy, eco…

Distributed neural tensor completion for network monitoring data recovery Abstract Network monitoring data is usually incomplete, accurate and fast recovery of missing data is of great significance for practical applications. The tensor-based nonlinear methods have attracted recent attentions with their capability of capturing complex interactions among data for more accurate recovery.