DATA RECOVERY Web Results

IRTF: A new tensor factorization for irregular multidimensional data ... By integrating the proposed IRTF and TV-CST, we establish an irregular multidimensional data recovery model. From (d), we can observe that our method preserves the color and the edge of the spatial-irregular multidimensional data as compared with traditional tensor factorization with preprocessing.

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.

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.

A novel tensor decomposition-based approach for internet traffic data ... Recovery and forecast of network traffic data from incomplete observed data is an important issue in internet engineering and management. In this pape…

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 …

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].

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.

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.

Bridge temperature data extraction and recovery based on physics-aided ... Therefore, it is necessary to develop effective methods to realize temperature data extraction and recovery. For the data with poor quality caused by sensor failure or noise, Posenato et al. [18] proposed a clustering-with-overlapping algorithm based on the correlation between the sensors.

Advances in direct liquid cooling technology and waste heat recovery ... 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.