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.

Adaptive and robust low-rank tensor completion for missing and ... Extensive experiments demonstrate the effectiveness, robustness, and adaptivity of the proposed method. In general, the proposed method achieves up to a 10 % and 12 % performance improvement in missing data imputation and missing and corrupted data recovery tasks respectively compared to the existing nonconvex LRTC method.

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 ... 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.

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.

Air quality index prediction through TimeGAN data recovery and PSO ... TimeGAN is employed to learn the structure and distribution of air quality data, facilitating the generation of synthetic data through adversarial networks. Subsequently, the missing values in the real data are replaced with the generated data.

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.

Trustworthy data recovery for incomplete multi-view learning Therefore, in this paper, we aim to develop a trustworthy data recovery framework for incomplete multi-view learning (TDR-IML), which ensures reliable imputed data with an uncertainty-aware scheme.

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.

Impacts of flexible-cooling and waste-heat recovery from data centres ... This paper quantifies the economic and environmental gains from data centre's cooling-based flexibility and waste-heat recovery in energy systems with significant data centre demand, high penetration of variable renewable sources, and well-established district heating networks.