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)
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
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
Advances in direct liquid cooling technology and waste heat recovery . . . Abstract The escalating demand for computational power and data storage has led to a substantial increase in the number and scale of data centers (DCs) worldwide Due to characteristics of high heat flux and reliable operational security, there is an urgent need for innovative solutions to solve the problem of DC cooling and waste heat recovery
Continuous strain missing data recovery with incomplete dataset using . . . Generally, missing data recovery is essentially regarded as a nonlinear regression analysis between the incomplete signal with data loss and the complete true signal Fan et al [15] employed a fully feedforward convolutional neural networks (CNN) with bottleneck architecture to capture the spatiotemporal relationships among the sensors data
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