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.

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.

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.

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.

Tensor robust principal component analysis with total generalized ... Compared with the general TRPCA recovery models which only consider the global prior information, our model considers both the global and local prior information of high-dimensional data for achieving satisfactory restoration performance.

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].

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.

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].

GRACE Follow-On accelerometer data recovery by high-precision ... The Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) satellites are equipped with high-precision three-axis accelerometers to measure all …

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.