DATA RECOVERY Web Results

The Missing Data Recovery Method Based on Improved GAN Most existing data recovery methods require complete datasets for training, leading to substantial data and computational demands and limited generalization. To address these limitations, this study proposes a missing data imputation model based on an improved Generative Adversarial Network (BAC-GAN).

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.

IRTF: A new tensor factorization for irregular multidimensional data ... Consequently, data recovery techniques are required to enhance the quality of these observed data for subsequent tasks. Real-world multidimensional data can be mathematically represented by the tensor [7], [8]. In this paper, we specifically focus on third-order tensors.

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 novel tensor decomposition-based approach for internet traffic data ... A series of numerical experiments about the recovery of structurally missing traffic data and the traffic data prediction on the widely used real-world datasets demonstrate that our approach have favorable performance than some state-of-the-art tensor/matrix based approaches.

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 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.

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.

Localized anomaly detection and recovery of marine engine data to ... In this research study SVD analysis is performed within data clusters for localized anomaly detection and missing data recovery. While localized models can effectively capture the nonlinear dynamics of the vessel, they are more straightforward to interpret and analyze the results compared to complex DL-based models.

Impacts of flexible-cooling and waste-heat recovery from data centres ... Nevertheless, data centres are also a potential sector-coupling interface supporting the energy transition through demand flexibility in electricity markets and waste-heat recovery for district heating systems.