DATA RECOVERY Web Results

Innovative approaches for deep decarbonization of data centers and ... 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.

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.

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.

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.

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].

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 ...

Tensor completion via joint reweighted tensor - ScienceDirect Tensor, as a generalization of vector and matrix, is a powerful tool for processing multi-dimensional data and plays a critical role in different fields of scientific computing, like color image and video inpainting [1], [2], [3], hyperspectral data recovery and classification [4], [5], [6], [7], and seismic data reconstruction [8].

Analysis of false lock in Mueller-Muller clock and data recovery system ... Within high-speed serial data communication systems, the Clock and Data Recovery (CDR) circuit serves as the core module. For SerDes, it is crucial to extract the clock signals at the receiver correctly for clock synchronization and optimal data sampling.