Dual-energy computed tomography (DECT) offers quantitative insights and facilitates material decomposition, aiding in precise diagnosis and treatment planning. However, existing methods for material ...

Microservices have an important position in today's software development, enabling a highly cohesive and low-coupling way of service organization. To address the complexity issues of inter-service ...

As a new technology for deploying applications and services in the cloud, microservice architecture is becoming a hot topic in IT architecture today. It overcomes the problems of low development ...

Workload prediction is one of the most basic requirements in developing cost and energy-efficient Cloud Data Centers (CDCs). Most traditional approaches have suffered from noise and failed to capture ...

Unit Commitment (UC) and Optimal Power Flow (OPF) are two fundamental problems in short-term electric power systems planning that are traditionally solved sequentially. The state-of-the-art mostly ...

Tensor ring (TR) decomposition demonstrates superior performance in handling high-order tensors. However, traditional TR-based decomposition algorithms face limitations in real-world applications due ...

For the two shortcomings of singular value decomposition (SVD), the determination of the reconstruction order and the poor noise reduction ability, an enhanced SVD is introduced in this article. The ...

This letter investigates ensemble median empirical mode decomposition (MEEMD), an extension model of ensemble empirical mode decomposition, and its improved characteristics for emotion recognition. It ...

Compared to other multivariate signal decomposition algorithms, the MvFIF-based method has demonstrated higher accuracy in recognizing emotions using multichannel EEG signals. The proposed ...

In Part I of this paper, we proposed and analyzed a novel algorithmic framework, termed penalty dual decomposition (PDD), for the minimization of a nonconvex nonsmooth objective function, subject to ...

Recently, there has been a growing interest in the exploration of Nonlinear Matrix Decomposition (NMD) due to its close ties with neural networks. NMD aims to find a low-rank matrix from a sparse ...

Choosing a decomposition method for multi-class classification is an important trade-off between efficiency and predictive accuracy. Trying all the decomposition methods to find the best one is too ...