Data Recovery

In recent times, data recovery has become increasingly relevant in various contexts. 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. 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. From another angle, 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… Innovative approaches for deep decarbonization of data centers and ....

In relation to this, the data center heat recovery systems discussed in the studies above fall into two primary categories: those utilizing heat pumps to recover waste heat from data centers for utilization in district heating networks or buildings, and those relying solely on heat exchangers. 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].

Furthermore, research on FDI attacks and their data recovery ... 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. Data Recovery - an overview | ScienceDirect Topics. Data recovery strategies include hot sites, spare or underutilized servers, the use of noncritical servers, duplicate data centers, replacement agreements, and transferring operations to other locations.

Lost data recovery for structural vibration data based on improved U .... Verification was conducted on single-channel and multi-channel data from practical engineering of large-span bridges by comparing the recovery levels in the time and frequency domains. Different missing ratios are set, a mask matrix is used to construct random lost data, and the proposed model is used to reconstruct the lost data. Demonstration of all-digital burst clock and data recovery for .... This perspective suggests that, we experimentally demonstrated all-digital burst clock and data recovery (BCDR) for symmetrical single-wavelength 50 Gb/s four-level amplitude modulat…

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].